Tree value system: users guide.

Treesearch

J.K. Ayer Sachet; D.G. Briggs; R.D. Fight

1989-01-01

This paper instructs resource analysts on use of the Tree Value System (TREEVAL). TREEVAL is a microcomputer system of programs for calculating tree or stand values and volumes based on predicted product recovery. Designed for analyzing silvicultural decisions, the system can also be used for appraisals and for evaluating log bucking. The system calculates results...

Interacting with mobile devices by fusion eye and hand gestures recognition systems based on decision tree approach

NASA Astrophysics Data System (ADS)

Elleuch, Hanene; Wali, Ali; Samet, Anis; Alimi, Adel M.

2017-03-01

Two systems of eyes and hand gestures recognition are used to control mobile devices. Based on a real-time video streaming captured from the device's camera, the first system recognizes the motion of user's eyes and the second one detects the static hand gestures. To avoid any confusion between natural and intentional movements we developed a system to fuse the decision coming from eyes and hands gesture recognition systems. The phase of fusion was based on decision tree approach. We conducted a study on 5 volunteers and the results that our system is robust and competitive.

Correlation Between the System Capabilities Analytic Process (SCAP) and the Missions and Means Framework (MMF)

DTIC Science & Technology

2013-05-01

specifics of the correlation will be explored followed by discussion of new paradigms— the ordered event list (OEL) and the decision tree — that result from...4.2.1  Brief Overview of the Decision Tree Paradigm ................................................15  4.2.2  OEL Explained...6  Figure 3. A depiction of a notional fault/activation tree . ................................................................7

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

NASA Technical Reports Server (NTRS)

Lee, Hanbong

2017-01-01

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

Analytical and CASE study on Limited Search, ID3, CHAID, C4.5, Improved C4.5 and OVA Decision Tree Algorithms to design Decision Support System

NASA Astrophysics Data System (ADS)

Kaur, Parneet; Singh, Sukhwinder; Garg, Sushil; Harmanpreet

2010-11-01

In this paper we study about classification algorithms for farm DSS. By applying classification algorithms i.e. Limited search, ID3, CHAID, C4.5, Improved C4.5 and One VS all Decision Tree on common data set of crop with specified class, results are obtained. The tool used to derive results is SPINA. The graphical results obtained from tool are compared to suggest best technique to develop farm Decision Support System. This analysis would help to researchers to design effective and fast DSS for farmer to take decision for enhancing their yield.

Multivariate analysis of flow cytometric data using decision trees.

PubMed

Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

2012-01-01

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

PubMed

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P < 0.01). A clinically useful classification tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

A dynamic fault tree model of a propulsion system

NASA Technical Reports Server (NTRS)

Xu, Hong; Dugan, Joanne Bechta; Meshkat, Leila

2006-01-01

We present a dynamic fault tree model of the benchmark propulsion system, and solve it using Galileo. Dynamic fault trees (DFT) extend traditional static fault trees with special gates to model spares and other sequence dependencies. Galileo solves DFT models using a judicious combination of automatically generated Markov and Binary Decision Diagram models. Galileo easily handles the complexities exhibited by the benchmark problem. In particular, Galileo is designed to model phased mission systems.

Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

PubMed Central

in ’t Veen, Johannes C.C.M.; Dekhuijzen, P.N. Richard; van Heijst, Ellen; Kocks, Janwillem W.H.; Muilwijk-Kroes, Jacqueline B.; Chavannes, Niels H.; van der Molen, Thys

2016-01-01

The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool. PMID:27730177

Decision support for mitigating the risk of tree induced transmission line failure in utility rights-of-way.

PubMed

Poulos, H M; Camp, A E

2010-02-01

Vegetation management is a critical component of rights-of-way (ROW) maintenance for preventing electrical outages and safety hazards resulting from tree contact with conductors during storms. Northeast Utility's (NU) transmission lines are a critical element of the nation's power grid; NU is therefore under scrutiny from federal agencies charged with protecting the electrical transmission infrastructure of the United States. We developed a decision support system to focus right-of-way maintenance and minimize the potential for a tree fall episode that disables transmission capacity across the state of Connecticut. We used field data on tree characteristics to develop a system for identifying hazard trees (HTs) in the field using limited equipment to manage Connecticut power line ROW. Results from this study indicated that the tree height-to-diameter ratio, total tree height, and live crown ratio were the key characteristics that differentiated potential risk trees (danger trees) from trees with a high probability of tree fall (HTs). Products from this research can be transferred to adaptive right-of-way management, and the methods we used have great potential for future application to other regions of the United States and elsewhere where tree failure can disrupt electrical power.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

PubMed

Lee, Saro; Park, Inhye

2013-09-30

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

PubMed

Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K

2008-07-01

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

A new approach to enhance the performance of decision tree for classifying gene expression data.

PubMed

Hassan, Md; Kotagiri, Ramamohanarao

2013-12-20

Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

PubMed

Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

2015-01-01

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

Safety validation of decision trees for hepatocellular carcinoma.

PubMed

Wang, Xian-Qiang; Liu, Zhe; Lv, Wen-Ping; Luo, Ying; Yang, Guang-Yun; Li, Chong-Hui; Meng, Xiang-Fei; Liu, Yang; Xu, Ke-Sen; Dong, Jia-Hong

2015-08-21

To evaluate a different decision tree for safe liver resection and verify its efficiency. A total of 2457 patients underwent hepatic resection between January 2004 and December 2010 at the Chinese PLA General Hospital, and 634 hepatocellular carcinoma (HCC) patients were eligible for the final analyses. Post-hepatectomy liver failure (PHLF) was identified by the association of prothrombin time < 50% and serum bilirubin > 50 μmol/L (the "50-50" criteria), which were assessed at day 5 postoperatively or later. The Swiss-Clavien decision tree, Tokyo University-Makuuchi decision tree, and Chinese consensus decision tree were adopted to divide patients into two groups based on those decision trees in sequence, and the PHLF rates were recorded. The overall mortality and PHLF rate were 0.16% and 3.0%. A total of 19 patients experienced PHLF. The numbers of patients to whom the Swiss-Clavien, Tokyo University-Makuuchi, and Chinese consensus decision trees were applied were 581, 573, and 622, and the PHLF rates were 2.75%, 2.62%, and 2.73%, respectively. Significantly more cases satisfied the Chinese consensus decision tree than the Swiss-Clavien decision tree and Tokyo University-Makuuchi decision tree (P < 0.01，P < 0.01); nevertheless, the latter two shared no difference (P = 0.147). The PHLF rate exhibited no significant difference with respect to the three decision trees. The Chinese consensus decision tree expands the indications for hepatic resection for HCC patients and does not increase the PHLF rate compared to the Swiss-Clavien and Tokyo University-Makuuchi decision trees. It would be a safe and effective algorithm for hepatectomy in patients with hepatocellular carcinoma.

Predicting metabolic syndrome using decision tree and support vector machine methods.

PubMed

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According to this study, in cases where only the final result of the decision is regarded significant, SVM method can be used with acceptable accuracy in decision making medical issues. This method has not been implemented in the previous research.

Rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems in the presence of trees.

PubMed

Scholz, Miklas; Uzomah, Vincent C

2013-08-01

The retrofitting of sustainable drainage systems (SuDS) such as permeable pavements is currently undertaken ad hoc using expert experience supported by minimal guidance based predominantly on hard engineering variables. There is a lack of practical decision support tools useful for a rapid assessment of the potential of ecosystem services when retrofitting permeable pavements in urban areas that either feature existing trees or should be planted with trees in the near future. Thus the aim of this paper is to develop an innovative rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems close to trees. This unique tool proposes the retrofitting of permeable pavements that obtained the highest ecosystem service score for a specific urban site enhanced by the presence of trees. This approach is based on a novel ecosystem service philosophy adapted to permeable pavements rather than on traditional engineering judgement associated with variables based on quick community and environment assessments. For an example case study area such as Greater Manchester, which was dominated by Sycamore and Common Lime, a comparison with the traditional approach of determining community and environment variables indicates that permeable pavements are generally a preferred SuDS option. Permeable pavements combined with urban trees received relatively high scores, because of their great potential impact in terms of water and air quality improvement, and flood control, respectively. The outcomes of this paper are likely to lead to more combined permeable pavement and tree systems in the urban landscape, which are beneficial for humans and the environment. Copyright © 2013 Elsevier B.V. All rights reserved.

A decision support system using combined-classifier for high-speed data stream in smart grid

NASA Astrophysics Data System (ADS)

Yang, Hang; Li, Peng; He, Zhian; Guo, Xiaobin; Fong, Simon; Chen, Huajun

2016-11-01

Large volume of high-speed streaming data is generated by big power grids continuously. In order to detect and avoid power grid failure, decision support systems (DSSs) are commonly adopted in power grid enterprises. Among all the decision-making algorithms, incremental decision tree is the most widely used one. In this paper, we propose a combined classifier that is a composite of a cache-based classifier (CBC) and a main tree classifier (MTC). We integrate this classifier into a stream processing engine on top of the DSS such that high-speed steaming data can be transformed into operational intelligence efficiently. Experimental results show that our proposed classifier can return more accurate answers than other existing ones.

Determinants of farmers' tree planting investment decision as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, B.; Bewket, W.; Bräuning, A.

2015-11-01

Land degradation due to lack of sustainable land management practices are one of the critical challenges in many developing countries including Ethiopia. This study explores the major determinants of farm level tree planting decision as a land management strategy in a typical framing and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and binary logistic regression model. The model significantly predicted farmers' tree planting decision (Chi-square = 37.29, df = 15, P<0.001). Besides, the computed significant value of the model suggests that all the considered predictor variables jointly influenced the farmers' decision to plant trees as a land management strategy. In this regard, the finding of the study show that local land-users' willingness to adopt tree growing decision is a function of a wide range of biophysical, institutional, socioeconomic and household level factors, however, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system have positively and significantly influence on tree growing investment decisions in the study watershed. Eventually, the processes of land use conversion and land degradation are serious which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, devising sustainable and integrated land management policy options and implementing them would enhance ecological restoration and livelihood sustainability in the study watershed.

Determinants of farmers' tree-planting investment decisions as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, Berhan; Bewket, Woldeamlak; Bräuning, Achim

2016-04-01

Land degradation due to lack of sustainable land management practices is one of the critical challenges in many developing countries including Ethiopia. This study explored the major determinants of farm-level tree-planting decisions as a land management strategy in a typical farming and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and a binary logistic regression model. The model significantly predicted farmers' tree-planting decisions (χ2 = 37.29, df = 15, P < 0.001). Besides, the computed significant value of the model revealed that all the considered predictor variables jointly influenced the farmers' decisions to plant trees as a land management strategy. The findings of the study demonstrated that the adoption of tree-growing decisions by local land users was a function of a wide range of biophysical, institutional, socioeconomic and household-level factors. In this regard, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system had a critical influence on tree-growing investment decisions in the study watershed. Eventually, the processes of land-use conversion and land degradation were serious, which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, the study recommended that devising and implementing sustainable land management policy options would enhance ecological restoration and livelihood sustainability in the study watershed.

A divide and conquer approach to cope with uncertainty, human health risk, and decision making in contaminant hydrology

NASA Astrophysics Data System (ADS)

de Barros, Felipe P. J.; Bolster, Diogo; Sanchez-Vila, Xavier; Nowak, Wolfgang

2011-05-01

Assessing health risk in hydrological systems is an interdisciplinary field. It relies on the expertise in the fields of hydrology and public health and needs powerful translation concepts to provide decision support and policy making. Reliable health risk estimates need to account for the uncertainties and variabilities present in hydrological, physiological, and human behavioral parameters. Despite significant theoretical advancements in stochastic hydrology, there is still a dire need to further propagate these concepts to practical problems and to society in general. Following a recent line of work, we use fault trees to address the task of probabilistic risk analysis and to support related decision and management problems. Fault trees allow us to decompose the assessment of health risk into individual manageable modules, thus tackling a complex system by a structural divide and conquer approach. The complexity within each module can be chosen individually according to data availability, parsimony, relative importance, and stage of analysis. Three differences are highlighted in this paper when compared to previous works: (1) The fault tree proposed here accounts for the uncertainty in both hydrological and health components, (2) system failure within the fault tree is defined in terms of risk being above a threshold value, whereas previous studies that used fault trees used auxiliary events such as exceedance of critical concentration levels, and (3) we introduce a new form of stochastic fault tree that allows us to weaken the assumption of independent subsystems that is required by a classical fault tree approach. We illustrate our concept in a simple groundwater-related setting.

An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity

NASA Technical Reports Server (NTRS)

Tian, Jianhui; Porter, Adam; Zelkowitz, Marvin V.

1992-01-01

Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their share of problems. A decision tree model was used to identify such modules. In this current paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement was tested using data from the NASA Software Engineering Laboratory.

Event Classification and Identification Based on the Characteristic Ellipsoid of Phasor Measurement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.

2011-09-23

In this paper, a method to classify and identify power system events based on the characteristic ellipsoid of phasor measurement is presented. The decision tree technique is used to perform the event classification and identification. Event types, event locations and clearance times are identified by decision trees based on the indices of the characteristic ellipsoid. A sufficiently large number of transient events were simulated on the New England 10-machine 39-bus system based on different system configurations. Transient simulations taking into account different event types, clearance times and various locations are conducted to simulate phasor measurement. Bus voltage magnitudes and recordedmore » reactive and active power flows are used to build the characteristic ellipsoid. The volume, eccentricity, center and projection of the longest axis in the parameter space coordinates of the characteristic ellipsoids are used to classify and identify events. Results demonstrate that the characteristic ellipsoid and the decision tree are capable to detect the event type, location, and clearance time with very high accuracy.« less

A tree biomass and carbon estimation system

Treesearch

Emily B. Schultz; Thomas G. Matney; Donald L. Grebner

2013-01-01

Appropriate forest management decisions for the developing woody biofuel and carbon credit markets require inventory and growth-and-yield systems reporting component tree dry weight biomass estimates. We have developed an integrated growth-and-yield and biomass/carbon calculator. The objective was to provide Mississippi’s State inventory system with bioenergy economic...

Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer.

PubMed

Suner, Aslı; Çelikoğlu, Can Cengiz; Dicle, Oğuz; Sökmen, Selman

2012-09-01

The aim of the study is to determine the most appropriate method for construction of a sequential decision tree in the management of rectal cancer, using various patient-specific criteria and treatments such as surgery, chemotherapy, and radiotherapy. An analytic hierarchy process (AHP) was used to determine the priorities of variables. Relevant criteria used in two decision steps and their relative priorities were established by a panel of five general surgeons. Data were collected via a web-based application and analyzed using the "Expert Choice" software specifically developed for the AHP. Consistency ratios in the AHP method were calculated for each set of judgments, and the priorities of sub-criteria were determined. A sequential decision tree was constructed for the best treatment decision process, using priorities determined by the AHP method. Consistency ratios in the AHP method were calculated for each decision step, and the judgments were considered consistent. The tumor-related criterion "presence of perforation" (0.331) and the patient-surgeon-related criterion "surgeon's experience" (0.630) had the highest priority in the first decision step. In the second decision step, the tumor-related criterion "the stage of the disease" (0.230) and the patient-surgeon-related criterion "surgeon's experience" (0.281) were the paramount criteria. The results showed some variation in the ranking of criteria between the decision steps. In the second decision step, for instance, the tumor-related criterion "presence of perforation" was just the fifth. The consistency of decision support systems largely depends on the quality of the underlying decision tree. When several choices and variables have to be considered in a decision, it is very important to determine priorities. The AHP method seems to be effective for this purpose. The decision algorithm developed by this method is more realistic and will improve the quality of the decision tree. Copyright © 2012 Elsevier B.V. All rights reserved.

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elter, M.; Schulz-Wendtland, R.; Wittenberg, T.

2007-11-15

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predictmore » breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value <0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z)=0.89{+-}0.01, the decision-tree approach in A(z)=0.87{+-}0.01, and the ANN approach in A(z)=0.88{+-}0.01.« less

Interpretable Categorization of Heterogeneous Time Series Data

NASA Technical Reports Server (NTRS)

Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

2017-01-01

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Using decision-tree classifier systems to extract knowledge from databases

NASA Technical Reports Server (NTRS)

St.clair, D. C.; Sabharwal, C. L.; Hacke, Keith; Bond, W. E.

1990-01-01

One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described.

Pollution mitigation and carbon sequestration by an urban forest.

PubMed

Brack, C L

2002-01-01

At the beginning of the 1900s, the Canberra plain was largely treeless. Graziers had carried out extensive clearing of the original trees since the 1820s leaving only scattered remnants and some plantings near homesteads. With the selection of Canberra as the site for the new capital of Australia, extensive tree plantings began in 1911. These trees have delivered a number of benefits, including aesthetic values and the amelioration of climatic extremes. Recently, however, it was considered that the benefits might extend to pollution mitigation and the sequestration of carbon. This paper outlines a case study of the value of the Canberra urban forest with particular reference to pollution mitigation. This study uses a tree inventory, modelling and decision support system developed to collect and use data about trees for tree asset management. The decision support system (DISMUT) was developed to assist in the management of about 400,000 trees planted in Canberra. The size of trees during the 5-year Kyoto Commitment Period was estimated using DISMUT and multiplied by estimates of value per square meter of canopy derived from available literature. The planted trees are estimated to have a combined energy reduction, pollution mitigation and carbon sequestration value of US$20-67 million during the period 2008-2012.

Fault trees for decision making in systems analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lambert, Howard E.

1975-10-09

The application of fault tree analysis (FTA) to system safety and reliability is presented within the framework of system safety analysis. The concepts and techniques involved in manual and automated fault tree construction are described and their differences noted. The theory of mathematical reliability pertinent to FTA is presented with emphasis on engineering applications. An outline of the quantitative reliability techniques of the Reactor Safety Study is given. Concepts of probabilistic importance are presented within the fault tree framework and applied to the areas of system design, diagnosis and simulation. The computer code IMPORTANCE ranks basic events and cut setsmore » according to a sensitivity analysis. A useful feature of the IMPORTANCE code is that it can accept relative failure data as input. The output of the IMPORTANCE code can assist an analyst in finding weaknesses in system design and operation, suggest the most optimal course of system upgrade, and determine the optimal location of sensors within a system. A general simulation model of system failure in terms of fault tree logic is described. The model is intended for efficient diagnosis of the causes of system failure in the event of a system breakdown. It can also be used to assist an operator in making decisions under a time constraint regarding the future course of operations. The model is well suited for computer implementation. New results incorporated in the simulation model include an algorithm to generate repair checklists on the basis of fault tree logic and a one-step-ahead optimization procedure that minimizes the expected time to diagnose system failure.« less

Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B.

PubMed

Chen, Xiao Yu; Ma, Li Zhuang; Chu, Na; Zhou, Min; Hu, Yiyang

2013-01-01

Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

An Approach for Implementing a Microcomputer Based Report Origination System in the Ada Programming Language

DTIC Science & Technology

1983-03-01

Decision Tree -------------------- 62 4-E. PACKAGE unitrep Action/Area Selection flow Chart 82 4-7. PACKAGE unitrep Control Flow Chart...the originetor wculd manually draft simple, readable, formatted iressages using "-i predef.ined forms and decision logic trees . This alternative was...Study Analysis DATA CCNTENT ERRORS PERCENT OF ERRORS Character Type 2.1 Calcvlations/Associations 14.3 Message Identification 4.? Value Pisiratch 22.E

TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

PubMed

Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

2018-01-01

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

VC-dimension of univariate decision trees.

PubMed

Yildiz, Olcay Taner

2015-02-01

In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and the number of inputs. Via a search algorithm that calculates the VC-dimension of univariate decision trees exhaustively, we show that our VC-dimension bounds are tight for simple trees. To verify that the VC-dimension bounds are useful, we also use them to get VC-generalization bounds for complexity control using structural risk minimization in decision trees, i.e., pruning. Our simulation results show that structural risk minimization pruning using the VC-dimension bounds finds trees that are more accurate as those pruned using cross validation.

Computerization of guidelines: a knowledge specification method to convert text to detailed decision tree for electronic implementation.

PubMed

Aguirre-Junco, Angel-Ricardo; Colombet, Isabelle; Zunino, Sylvain; Jaulent, Marie-Christine; Leneveut, Laurence; Chatellier, Gilles

2004-01-01

The initial step for the computerization of guidelines is the knowledge specification from the prose text of guidelines. We describe a method of knowledge specification based on a structured and systematic analysis of text allowing detailed specification of a decision tree. We use decision tables to validate the decision algorithm and decision trees to specify and represent this algorithm, along with elementary messages of recommendation. Edition tools are also necessary to facilitate the process of validation and workflow between expert physicians who will validate the specified knowledge and computer scientist who will encode the specified knowledge in a guide-line model. Applied to eleven different guidelines issued by an official agency, the method allows a quick and valid computerization and integration in a larger decision support system called EsPeR (Personalized Estimate of Risks). The quality of the text guidelines is however still to be developed further. The method used for computerization could help to define a framework usable at the initial step of guideline development in order to produce guidelines ready for electronic implementation.

The Decision Tree: A Tool for Achieving Behavioral Change.

ERIC Educational Resources Information Center

Saren, Dru

1999-01-01

Presents a "Decision Tree" process for structuring team decision making and problem solving about specific student behavioral goals. The Decision Tree involves a sequence of questions/decisions that can be answered in "yes/no" terms. Questions address reasonableness of the goal, time factors, importance of the goal, responsibilities, safety,…

PVEX: An expert system for producibility/value engineering

NASA Technical Reports Server (NTRS)

Lam, Chun S.; Moseley, Warren

1991-01-01

PVEX is described as an expert system that solves the problem of selection of the material and process in missile manufacturing. The producibility and the value problem has been deeply studied in the past years, and was written in dBase III and PROLOG before. A new approach is presented in that the solution is achieved by introducing hypothetical reasoning, heuristic criteria integrated with a simple hypertext system and shell programming. PVEX combines KMS with Unix scripts which graphically depicts decision trees. The decision trees convey high level qualitative problem solving knowledge to users, and a stand-alone help facility and technical documentation is available through KMS. The system developed is considerably less development costly than any other comparable expert system.

Development and acceptability testing of decision trees for self-management of prosthetic socket fit in adults with lower limb amputation.

PubMed

Lee, Daniel Joseph; Veneri, Diana A

2018-05-01

The most common complaint lower limb prosthesis users report is inadequacy of a proper socket fit. Adjustments to the residual limb-socket interface can be made by the prosthesis user without consultation of a clinician in many scenarios through skilled self-management. Decision trees guide prosthesis wearers through the self-management process, empowering them to rectify fit issues, or referring them to a clinician when necessary. This study examines the development and acceptability testing of patient-centered decision trees for lower limb prosthesis users. Decision trees underwent a four-stage process: literature review and expert consultation, designing, two-rounds of expert panel review and revisions, and target audience testing. Fifteen lower limb prosthesis users (average age 61 years) reviewed the decision trees and completed an acceptability questionnaire. Participants reported agreement of 80% or above in five of the eight questions related to acceptability of the decision trees. Disagreement was related to the level of experience of the respondent. Decision trees were found to be easy to use, illustrate correct solutions to common issues, and have terminology consistent with that of a new prosthesis user. Some users with greater than 1.5 years of experience would not use the decision trees based on their own self-management skills. Implications for Rehabilitation Discomfort of the residual limb-prosthetic socket interface is the most common reason for clinician visits. Prosthesis users can use decision trees to guide them through the process of obtaining a proper socket fit independently. Newer users may benefit from using the decision trees more than experienced users.

Minimizing the cost of translocation failure with decision-tree models that predict species' behavioral response in translocation sites.

PubMed

Ebrahimi, Mehregan; Ebrahimie, Esmaeil; Bull, C Michael

2015-08-01

The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision-tree models for species' translocation, we used data on the short-term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision-tree algorithms (decision tree, decision-tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. © 2015 Society for Conservation Biology.

Decision trees in epidemiological research.

PubMed

Venkatasubramaniam, Ashwini; Wolfson, Julian; Mitchell, Nathan; Barnes, Timothy; JaKa, Meghan; French, Simone

2017-01-01

In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

Advanced Subspace Techniques for Modeling Channel and Session Variability in a Speaker Recognition System

DTIC Science & Technology

2012-03-01

with each SVM discriminating between a pair of the N total speakers in the data set. The (( + 1))/2 classifiers then vote on the final...classification of a test sample. The Random Forest classifier is an ensemble classifier that votes amongst decision trees generated with each node using...Forest vote , and the effects of overtraining will be mitigated by the fact that each decision tree is overtrained differently (due to the random

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

Bioinformatics in proteomics: application, terminology, and pitfalls.

PubMed

Wiemer, Jan C; Prokudin, Alexander

2004-01-01

Bioinformatics applies data mining, i.e., modern computer-based statistics, to biomedical data. It leverages on machine learning approaches, such as artificial neural networks, decision trees and clustering algorithms, and is ideally suited for handling huge data amounts. In this article, we review the analysis of mass spectrometry data in proteomics, starting with common pre-processing steps and using single decision trees and decision tree ensembles for classification. Special emphasis is put on the pitfall of overfitting, i.e., of generating too complex single decision trees. Finally, we discuss the pros and cons of the two different decision tree usages.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Siting a municipal solid waste disposal facility, part II: the effects of external criteria on the final decision.

PubMed

Korucu, M Kemal; Karademir, Aykan

2014-02-01

The procedure of a multi-criteria decision analysis supported by the geographic information systems was applied to the site selection process of a planning municipal solid waste management practice based on twelve different scenarios. The scenarios included two different decision tree modes and two different weighting models for three different area requirements. The suitability rankings of the suitable sites obtained from the application of the decision procedure for the scenarios were assessed by a factorial experimental design concerning the effect of some external criteria on the final decision of the site selection process. The external criteria used in the factorial experimental design were defined as "Risk perception and approval of stakeholders" and "Visibility". The effects of the presence of these criteria in the decision trees were evaluated in detail. For a quantitative expression of the differentiations observed in the suitability rankings, the ranking data were subjected to ANOVA test after a normalization process. Then the results of these tests were evaluated by Tukey test to measure the effects of external criteria on the final decision. The results of Tukey tests indicated that the involvement of the external criteria into the decision trees produced statistically meaningful differentiations in the suitability rankings. Since the external criteria could cause considerable external costs during the operation of the disposal facilities, the presence of these criteria in the decision tree in addition to the other criteria related to environmental and legislative requisites could prevent subsequent external costs in the first place.

Application of Decision Tree on Collision Avoidance System Design and Verification for Quadcopter

NASA Astrophysics Data System (ADS)

Chen, C.-W.; Hsieh, P.-H.; Lai, W.-H.

2017-08-01

The purpose of the research is to build a collision avoidance system with decision tree algorithm used for quadcopters. While the ultrasonic range finder judges the distance is in collision avoidance interval, the access will be replaced from operator to the system to control the altitude of the UAV. According to the former experiences on operating quadcopters, we can obtain the appropriate pitch angle. The UAS implement the following three motions to avoid collisions. Case1: initial slow avoidance stage, Case2: slow avoidance stage and Case3: Rapid avoidance stage. Then the training data of collision avoidance test will be transmitted to the ground station via wireless transmission module to further analysis. The entire decision tree algorithm of collision avoidance system, transmission data, and ground station have been verified in some flight tests. In the flight test, the quadcopter can implement avoidance motion in real-time and move away from obstacles steadily. In the avoidance area, the authority of the collision avoidance system is higher than the operator and implements the avoidance process. The quadcopter can successfully fly away from the obstacles in 1.92 meter per second and the minimum distance between the quadcopter and the obstacle is 1.05 meters.

Improving ensemble decision tree performance using Adaboost and Bagging

NASA Astrophysics Data System (ADS)

Hasan, Md. Rajib; Siraj, Fadzilah; Sainin, Mohd Shamrie

2015-12-01

Ensemble classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the ensemble method as it is proven to be better than single classifiers. However, in a ensemble settings the performance depends on the selection of suitable base classifier. This research employed two prominent esemble s namely Adaboost and Bagging with base classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different base classifiers are selected and even some places overfitting issue also been noted. The evidence shows that ensemble decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

PubMed

Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

2015-11-01

Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. © 2015 American Academy of Forensic Sciences.

Multi-test decision tree and its application to microarray data classification.

PubMed

Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

2014-05-01

The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

Ontology based decision system for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

PubMed

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Situation-Assessment And Decision-Aid Production-Rule Analysis System For Nuclear Plant Monitoring And Emergency Preparedness

NASA Astrophysics Data System (ADS)

Gvillo, D.; Ragheb, M.; Parker, M.; Swartz, S.

1987-05-01

A Production-Rule Analysis System is developed for Nuclear Plant Monitoring. The signals generated by the Zion-1 Plant are considered. A Situation-Assessment and Decision-Aid capability is provided for monitoring the integrity of the Plant Radiation, the Reactor Coolant, the Fuel Clad, and the Containment Systems. A total of 41 signals are currently fed as facts to an Inference Engine functioning in the backward-chaining mode and built along the same structure as the E-Mycin system. The Goal-Tree constituting the Knowledge Base was generated using a representation in the form of Fault Trees deduced from plant procedures information. The system is constructed in support of the Data Analysis and Emergency Preparedness tasks at the Illinois Radiological Emergency Assessment Center (REAC).

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images.

PubMed

Mane, Vijay Mahadeo; Jadhav, D V

2017-05-24

Diabetic retinopathy (DR) is the most common diabetic eye disease. Doctors are using various test methods to detect DR. But, the availability of test methods and requirements of domain experts pose a new challenge in the automatic detection of DR. In order to fulfill this objective, a variety of algorithms has been developed in the literature. In this paper, we propose a system consisting of a novel sparking process and a holoentropy-based decision tree for automatic classification of DR images to further improve the effectiveness. The sparking process algorithm is developed for automatic segmentation of blood vessels through the estimation of optimal threshold. The holoentropy enabled decision tree is newly developed for automatic classification of retinal images into normal or abnormal using hybrid features which preserve the disease-level patterns even more than the signal level of the feature. The effectiveness of the proposed system is analyzed using standard fundus image databases DIARETDB0 and DIARETDB1 for sensitivity, specificity and accuracy. The proposed system yields sensitivity, specificity and accuracy values of 96.72%, 97.01% and 96.45%, respectively. The experimental result reveals that the proposed technique outperforms the existing algorithms.

Using Unix system auditing for detecting network intrusions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christensen, M.J.

1993-03-01

Intrusion Detection Systems (IDSs) are designed to detect actions of individuals who use computer resources without authorization as well as legitimate users who exceed their privileges. This paper describes a novel approach to IDS research, namely a decision aiding approach to intrusion detection. The introduction of a decision tree represents the logical steps necessary to distinguish and identify different types of attacks. This tool, the Intrusion Decision Aiding Tool (IDAT), utilizes IDS-based attack models and standard Unix audit data. Since attacks have certain characteristics and are based on already developed signature attack models, experienced and knowledgeable Unix system administrators knowmore » what to look for in system audit logs to determine if a system has been attacked. Others, however, are usually less able to recognize common signatures of unauthorized access. Users can traverse the tree using available audit data displayed by IDAT and general knowledge they possess to reach a conclusion regarding suspicious activity. IDAT is an easy-to-use window based application that gathers, analyzes, and displays pertinent system data according to Unix attack characteristics. IDAT offers a more practical approach and allows the user to make an informed decision regarding suspicious activity.« less

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

Circum-Arctic petroleum systems identified using decision-tree chemometrics

USGS Publications Warehouse

Peters, K.E.; Ramos, L.S.; Zumberge, J.E.; Valin, Z.C.; Scotese, C.R.; Gautier, D.L.

2007-01-01

Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55??N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circumArctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Decision and Game Theory for Security

NASA Astrophysics Data System (ADS)

Alpcan, Tansu; Buttyán, Levente; Baras, John S.

Attack--defense trees are used to describe security weaknesses of a system and possible countermeasures. In this paper, the connection between attack--defense trees and game theory is made explicit. We show that attack--defense trees and binary zero-sum two-player extensive form games have equivalent expressive power when considering satisfiability, in the sense that they can be converted into each other while preserving their outcome and their internal structure.

Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

NASA Technical Reports Server (NTRS)

Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

2009-01-01

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

Objective consensus from decision trees.

PubMed

Putora, Paul Martin; Panje, Cedric M; Papachristofilou, Alexandros; Dal Pra, Alan; Hundsberger, Thomas; Plasswilm, Ludwig

2014-12-05

Consensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources. Based on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus. Based on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters. Recommendations represented as decision trees can serve as a basis for objective consensus among multiple parties.

Divide and Conquer: A Valid Approach for Risk Assessment and Decision Making under Uncertainty for Groundwater-Related Diseases

NASA Astrophysics Data System (ADS)

Sanchez-Vila, X.; de Barros, F.; Bolster, D.; Nowak, W.

2010-12-01

Assessing the potential risk of hydro(geo)logical supply systems to human population is an interdisciplinary field. It relies on the expertise in fields as distant as hydrogeology, medicine, or anthropology, and needs powerful translation concepts to provide decision support and policy making. Reliable health risk estimates need to account for the uncertainties in hydrological, physiological and human behavioral parameters. We propose the use of fault trees to address the task of probabilistic risk analysis (PRA) and to support related management decisions. Fault trees allow decomposing the assessment of health risk into individual manageable modules, thus tackling a complex system by a structural “Divide and Conquer” approach. The complexity within each module can be chosen individually according to data availability, parsimony, relative importance and stage of analysis. The separation in modules allows for a true inter- and multi-disciplinary approach. This presentation highlights the three novel features of our work: (1) we define failure in terms of risk being above a threshold value, whereas previous studies used auxiliary events such as exceedance of critical concentration levels, (2) we plot an integrated fault tree that handles uncertainty in both hydrological and health components in a unified way, and (3) we introduce a new form of stochastic fault tree that allows to weaken the assumption of independent subsystems that is required by a classical fault tree approach. We illustrate our concept in a simple groundwater-related setting.

The decision tree approach to classification

NASA Technical Reports Server (NTRS)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

An evaluation of consensus techniques for diagnostic interpretation

NASA Astrophysics Data System (ADS)

Sauter, Jake N.; LaBarre, Victoria M.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Learning diagnostic labels from image content has been the standard in computer-aided diagnosis. Most computer-aided diagnosis systems use low-level image features extracted directly from image content to train and test machine learning classifiers for diagnostic label prediction. When the ground truth for the diagnostic labels is not available, reference truth is generated from the experts diagnostic interpretations of the image/region of interest. More specifically, when the label is uncertain, e.g. when multiple experts label an image and their interpretations are different, techniques to handle the label variability are necessary. In this paper, we compare three consensus techniques that are typically used to encode the variability in the experts labeling of the medical data: mean, median and mode, and their effects on simple classifiers that can handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees). Given that the NIH/NCI Lung Image Database Consortium (LIDC) data provides interpretations for lung nodules by up to four radiologists, we leverage the LIDC data to evaluate and compare these consensus approaches when creating computer-aided diagnosis systems for lung nodules. First, low-level image features of nodules are extracted and paired with their radiologists semantic ratings (1= most likely benign, , 5 = most likely malignant); second, machine learning multi-class classifiers that handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees) are built to predict the lung nodules semantic ratings. We show that the mean-based consensus generates the most robust classi- fier overall when compared to the median- and mode-based consensus. Lastly, the results of this study show that, when building CAD systems with uncertain diagnostic interpretation, it is important to evaluate different strategies for encoding and predicting the diagnostic label.

Decision tree and ensemble learning algorithms with their applications in bioinformatics.

PubMed

Che, Dongsheng; Liu, Qi; Rasheed, Khaled; Tao, Xiuping

2011-01-01

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.

A Decision Tree for Psychology Majors: Supplying Questions as Well as Answers.

ERIC Educational Resources Information Center

Poe, Retta E.

1988-01-01

Outlines the development of a psychology careers decision tree to help faculty advise students plan their program. States that students using the decision tree may benefit by learning more about their career options and by acquiring better question-asking skills. (GEA)

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

Policy Tree Optimization for Adaptive Management of Water Resources Systems

NASA Astrophysics Data System (ADS)

Herman, J. D.; Giuliani, M.

2016-12-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points", which are threshold values of indicator variables that signal a change in policy. However, there remains a need for a general method to optimize the choice of indicators and their threshold values in a way that is easily interpretable for decision makers. Here we propose a conceptual framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. We demonstrate the approach using Folsom Reservoir, California as a case study, in which operating policies must balance the risk of both floods and droughts. Given a set of feature variables, such as reservoir level, inflow observations and forecasts, and time of year, the resulting policy defines the conditions under which flood control and water supply hedging operations should be triggered. Importantly, the tree-based rule sets are easy to interpret for decision making, and can be compared to historical operating policies to understand the adaptations needed under possible climate change scenarios. Several remaining challenges are discussed, including the empirical convergence properties of the method, and extensions to irreversible decisions such as infrastructure. Policy tree optimization, and corresponding open-source software, provide a generalizable, interpretable approach to designing adaptive policies under uncertainty for water resources systems.

The value of decision tree analysis in planning anaesthetic care in obstetrics.

PubMed

Bamber, J H; Evans, S A

2016-08-01

The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evolutionary Algorithm Based Automated Reverse Engineering and Defect Discovery

DTIC Science & Technology

2007-09-21

a previous application of a GP as a data mining function to evolve fuzzy decision trees symbolically [3-5], the terminal set consisted of fuzzy...of input and output information is required. In the case of fuzzy decision trees, the database represented a collection of scenarios about which the...fuzzy decision tree to be evolved would make decisions . The database also had entries created by experts representing decisions about the scenarios

Knowledge assisted diagnosis of mood disorders using DSM-3

NASA Technical Reports Server (NTRS)

Fritz, Robert H.

1990-01-01

As part of an Expert Systems class at the University of Houston Clear Lake, a system has been developed using CLIPS to allow a clinical psychologist or psychiatrist to diagnose mood disturbances by providing answers to questions corresponding to branches of a DSM-III criteria tree. Experienced clinicians may assert indications of the client's behavior in order to circumvent multiple levels of the tree, thus speeding diagnosis. An explanation facility was developed for validation of the diagnosis . It also allows for 'what if' scenarios by allowing the clinician to move backwards from the diagnosis to any decision branch and alter the answer previously provided. The system was implemented with a limited vocabulary of symptoms associated primarily with depressive disorders. However, the design supports the addition of vocabulary modules and knowledge bases for other types of disorders. The system currently has applicability in an instructional setting. With the addition of a more complete vocabulary, it could have applicability in a clinical setting. The overall design will support any application where determinations are made via a decision tree.

The decision tree classifier - Design and potential. [for Landsat-1 data

NASA Technical Reports Server (NTRS)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Mapping the Delivery of Societal Benefit through the International Arctic Observations Assessment Framework

NASA Astrophysics Data System (ADS)

Lev, S. M.; Gallo, J.

2017-12-01

The international Arctic scientific community has identified the need for a sustained and integrated portfolio of pan-Arctic Earth-observing systems. In 2017, an international effort was undertaken to develop the first ever Value Tree framework for identifying common research and operational objectives that rely on Earth observation data derived from Earth-observing systems, sensors, surveys, networks, models, and databases to deliver societal benefits in the Arctic. A Value Tree Analysis is a common tool used to support decision making processes and is useful for defining concepts, identifying objectives, and creating a hierarchical framework of objectives. A multi-level societal benefit area value tree establishes the connection from societal benefits to the set of observation inputs that contribute to delivering those benefits. A Value Tree that relies on expert domain knowledge from Arctic and non-Arctic nations, international researchers, Indigenous knowledge holders, and other experts to develop a framework to serve as a logical and interdependent decision support tool will be presented. Value tree examples that map the contribution of Earth observations in the Arctic to achieving societal benefits will be presented in the context of the 2017 International Arctic Observations Assessment Framework. These case studies will highlight specific observing products and capability groups where investment is needed to contribute to the development of a sustained portfolio of Arctic observing systems.

Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.

PubMed

Nakas, Christos T; Schütz, Narayan; Werners, Marcus; Leichtle, Alexander B

2016-01-01

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures.

Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

ERIC Educational Resources Information Center

Lafond, Daniel; Lacouture, Yves; Cohen, Andrew L.

2009-01-01

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to…

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

PubMed Central

Masías, Víctor H.; Krause, Mariane; Valdés, Nelson; Pérez, J. C.; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice. PMID:25914657

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process.

PubMed

Masías, Víctor H; Krause, Mariane; Valdés, Nelson; Pérez, J C; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

A decision tree-based on-line preventive control strategy for power system transient instability prevention

NASA Astrophysics Data System (ADS)

Xu, Yan; Dong, Zhao Yang; Zhang, Rui; Wong, Kit Po

2014-02-01

Maintaining transient stability is a basic requirement for secure power system operations. Preventive control deals with modifying the system operating point to withstand probable contingencies. In this article, a decision tree (DT)-based on-line preventive control strategy is proposed for transient instability prevention of power systems. Given a stability database, a distance-based feature estimation algorithm is first applied to identify the critical generators, which are then used as features to develop a DT. By interpreting the splitting rules of DT, preventive control is realised by formulating the rules in a standard optimal power flow model and solving it. The proposed method is transparent in control mechanism, on-line computation compatible and convenient to deal with multi-contingency. The effectiveness and efficiency of the method has been verified on New England 10-machine 39-bus test system.

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

PubMed

Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

2014-01-01

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

PubMed

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree

NASA Astrophysics Data System (ADS)

Wahyuni, Sri

2018-03-01

Data mining was the process of finding useful information from a large set of databases. One of the existing techniques in data mining was classification. The method used was decision tree method and algorithm used was C4.5 algorithm. The decision tree method was a method that transformed a very large fact into a decision tree which was presenting the rules. Decision tree method was useful for exploring data, as well as finding a hidden relationship between a number of potential input variables with a target variable. The decision tree of the C4.5 algorithm was constructed with several stages including the selection of attributes as roots, created a branch for each value and divided the case into the branch. These stages would be repeated for each branch until all the cases on the branch had the same class. From the solution of the decision tree there would be some rules of a case. In this case the researcher classified the data of prisoners at Labuhan Deli prison to know the factors of detainees committing criminal acts of drugs. By applying this C4.5 algorithm, then the knowledge was obtained as information to minimize the criminal acts of drugs. From the findings of the research, it was found that the most influential factor of the detainee committed the criminal act of drugs was from the address variable.

An Improved Decision Tree for Predicting a Major Product in Competing Reactions

ERIC Educational Resources Information Center

Graham, Kate J.

2014-01-01

When organic chemistry students encounter competing reactions, they are often overwhelmed by the task of evaluating multiple factors that affect the outcome of a reaction. The use of a decision tree is a useful tool to teach students to evaluate a complex situation and propose a likely outcome. Specifically, a decision tree can help students…

Decision Tree Phytoremediation

DTIC Science & Technology

1999-12-01

aromatic hydrocarbons, and landfill leachates . Phytoremediation has been used for point and nonpoint source hazardous waste control. 1.2 Types of... Phytoremediation Prepared by Interstate Technology and Regulatory Cooperation Work Group Phytoremediation Work Team December 1999 Decision Tree...1999 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Phytoremediation Decision Tree 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c

Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining.

PubMed

Habibi, Shafi; Ahmadi, Maryam; Alizadeh, Somayeh

2015-03-18

The aim of this study was to examine a predictive model using features related to the diabetes type 2 risk factors. The data were obtained from a database in a diabetes control system in Tabriz, Iran. The data included all people referred for diabetes screening between 2009 and 2011. The features considered as "Inputs" were: age, sex, systolic and diastolic blood pressure, family history of diabetes, and body mass index (BMI). Moreover, we used diagnosis as "Class". We applied the "Decision Tree" technique and "J48" algorithm in the WEKA (3.6.10 version) software to develop the model. After data preprocessing and preparation, we used 22,398 records for data mining. The model precision to identify patients was 0.717. The age factor was placed in the root node of the tree as a result of higher information gain. The ROC curve indicates the model function in identification of patients and those individuals who are healthy. The curve indicates high capability of the model, especially in identification of the healthy persons. We developed a model using the decision tree for screening T2DM which did not require laboratory tests for T2DM diagnosis.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

PubMed

Barros, Rodrigo C; Winck, Ana T; Machado, Karina S; Basgalupp, Márcio P; de Carvalho, André C P L F; Ruiz, Duncan D; de Souza, Osmar Norberto

2012-11-21

This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

PubMed Central

2012-01-01

Background This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor. PMID:23171000

Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

NASA Astrophysics Data System (ADS)

Chan, Joseph C. C.; Lin, Tsau Y.

2001-03-01

This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree.

PubMed

Pärkkä, Juha; Cluitmans, Luc; Ermes, Miikka

2010-09-01

Inactive and sedentary lifestyle is a major problem in many industrialized countries today. Automatic recognition of type of physical activity can be used to show the user the distribution of his daily activities and to motivate him into more active lifestyle. In this study, an automatic activity-recognition system consisting of wireless motion bands and a PDA is evaluated. The system classifies raw sensor data into activity types online. It uses a decision tree classifier, which has low computational cost and low battery consumption. The classifier parameters can be personalized online by performing a short bout of an activity and by telling the system which activity is being performed. Data were collected with seven volunteers during five everyday activities: lying, sitting/standing, walking, running, and cycling. The online system can detect these activities with overall 86.6% accuracy and with 94.0% accuracy after classifier personalization.

Office of Legacy Management Decision Tree for Solar Photovoltaic Projects - 13317

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elmer, John; Butherus, Michael; Barr, Deborah L.

2013-07-01

To support consideration of renewable energy power development as a land reuse option, the DOE Office of Legacy Management (LM) and the National Renewable Energy Laboratory (NREL) established a partnership to conduct an assessment of wind and solar renewable energy resources on LM lands. From a solar capacity perspective, the larger sites in the western United States present opportunities for constructing solar photovoltaic (PV) projects. A detailed analysis and preliminary plan was developed for three large sites in New Mexico, assessing the costs, the conceptual layout of a PV system, and the electric utility interconnection process. As a result ofmore » the study, a 1,214-hectare (3,000-acre) site near Grants, New Mexico, was chosen for further study. The state incentives, utility connection process, and transmission line capacity were key factors in assessing the feasibility of the project. LM's Durango, Colorado, Disposal Site was also chosen for consideration because the uranium mill tailings disposal cell is on a hillside facing south, transmission lines cross the property, and the community was very supportive of the project. LM worked with the regulators to demonstrate that the disposal cell's long-term performance would not be impacted by the installation of a PV solar system. A number of LM-unique issues were resolved in making the site available for a private party to lease a portion of the site for a solar PV project. A lease was awarded in September 2012. Using a solar decision tree that was developed and launched by the EPA and NREL, LM has modified and expanded the decision tree structure to address the unique aspects and challenges faced by LM on its multiple sites. The LM solar decision tree covers factors such as land ownership, usable acreage, financial viability of the project, stakeholder involvement, and transmission line capacity. As additional sites are transferred to LM in the future, the decision tree will assist in determining whether a solar PV project is feasible on the new sites. (authors)« less

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

NASA Astrophysics Data System (ADS)

Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.

2017-12-01

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. Since sub-daily streamflow information is unavailable for most small basins in China, one of the main challenges is finding appropriate parameter values for simulating flash floods in ungauged catchments. In this study, we use decision tree learning to explore parameter set transferability between different catchments. For this purpose, the physically-based, semi-distributed rainfall-runoff model PRMS-OMS is set up for 35 catchments in ten Chinese provinces. Hourly data from more than 800 storm runoff events are used to calibrate the model and evaluate the performance of parameter set transfers between catchments. For each catchment, 58 catchment attributes are extracted from several data sets available for whole China. We then use a data mining technique (decision tree learning) to identify catchment similarities that can be related to good transfer performance. Finally, we use the splitting rules of decision trees for finding suitable donor catchments for ungauged target catchments. We show that decision tree learning allows to optimally utilize the information content of available catchment descriptors and outperforms regionalization based on a conventional measure of physiographic-climatic similarity by 15%-20%. Similar performance can be achieved with a regionalization method based on spatial proximity, but decision trees offer flexible rules for selecting suitable donor catchments, not relying on the vicinity of gauged catchments. This flexibility makes the method particularly suitable for implementation in sparsely gauged environments. We evaluate the probability to detect flood events exceeding a given return period, considering measured discharge and PRMS-OMS simulated flows with regionalized parameters. Overall, the probability of detection of an event with a return period of 10 years is 62%. 44% of all 10-year flood peaks can be detected with a timing error of 2 hours or less. These results indicate that the modeling system can provide useful information about the timing and magnitude of flood events at ungauged sites.

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 2 2013-01-01 2013-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 2 2014-01-01 2014-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 2 2012-01-01 2012-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 2 2011-01-01 2011-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

Improved Frame Mode Selection for AMR-WB+ Based on Decision Tree

NASA Astrophysics Data System (ADS)

Kim, Jong Kyu; Kim, Nam Soo

In this letter, we propose a coding mode selection method for the AMR-WB+ audio coder based on a decision tree. In order to reduce computation while maintaining good performance, decision tree classifier is adopted with the closed loop mode selection results as the target classification labels. The size of the decision tree is controlled by pruning, so the proposed method does not increase the memory requirement significantly. Through an evaluation test on a database covering both speech and music materials, the proposed method is found to achieve a much better mode selection accuracy compared with the open loop mode selection module in the AMR-WB+.

Activity classification using realistic data from wearable sensors.

PubMed

Pärkkä, Juha; Ermes, Miikka; Korpipää, Panu; Mäntyjärvi, Jani; Peltola, Johannes; Korhonen, Ilkka

2006-01-01

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

Annosus Root Disease Hazard Rating, Detection, and Management Strategies in the Southeastern United States

Treesearch

S. A. Alexander

1989-01-01

Annosus root disease (ARD), is the major root disease of pines in the southeastern United States where severely affected trees exhibit growth loss. Assessing the potential damage of ARD is essential for making effective disease control and management decisions. A soil hazard rating system developed to identify potential for tree mortality is described. The Annosus...

Groundwater Circulating Well Assessment and Guidance

DTIC Science & Technology

1998-04-03

47 3 . 1 Decis ion Tree and Process Description...two GCW systems p laced c lose enough to affect each other significantly (Herding et al. , 1 994). This type of wel l spaci ng may be requ ired to...3.1 Decision Tree and Process Description The process for screening the GCW technology is a logical sequence of steps during which site­ specific

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

PubMed

Kamphuis, C; Mollenhorst, H; Heesterbeek, J A P; Hogeveen, H

2010-08-01

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Discriminant forest classification method and system

DOEpatents

Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

An Isometric Mapping Based Co-Location Decision Tree Algorithm

NASA Astrophysics Data System (ADS)

Zhou, G.; Wei, J.; Zhou, X.; Zhang, R.; Huang, W.; Sha, H.; Chen, J.

2018-05-01

Decision tree (DT) induction has been widely used in different pattern classification. However, most traditional DTs have the disadvantage that they consider only non-spatial attributes (ie, spectral information) as a result of classifying pixels, which can result in objects being misclassified. Therefore, some researchers have proposed a co-location decision tree (Cl-DT) method, which combines co-location and decision tree to solve the above the above-mentioned traditional decision tree problems. Cl-DT overcomes the shortcomings of the existing DT algorithms, which create a node for each value of a given attribute, which has a higher accuracy than the existing decision tree approach. However, for non-linearly distributed data instances, the euclidean distance between instances does not reflect the true positional relationship between them. In order to overcome these shortcomings, this paper proposes an isometric mapping method based on Cl-DT (called, (Isomap-based Cl-DT), which is a method that combines heterogeneous and Cl-DT together. Because isometric mapping methods use geodetic distances instead of Euclidean distances between non-linearly distributed instances, the true distance between instances can be reflected. The experimental results and several comparative analyzes show that: (1) The extraction method of exposed carbonate rocks is of high accuracy. (2) The proposed method has many advantages, because the total number of nodes, the number of leaf nodes and the number of nodes are greatly reduced compared to Cl-DT. Therefore, the Isomap -based Cl-DT algorithm can construct a more accurate and faster decision tree.

Development of a New Decision Tree to Rapidly Screen Chemical Estrogenic Activities of Xenopus laevis.

PubMed

Wang, Ting; Li, Weiying; Zheng, Xiaofeng; Lin, Zhifen; Kong, Deyang

2014-02-01

During the last past decades, there is an increasing number of studies about estrogenic activities of the environmental pollutants on amphibians and many determination methods have been proposed. However, these determination methods are time-consuming and expensive, and a rapid and simple method to screen and test the chemicals for estrogenic activities to amphibians is therefore imperative. Herein is proposed a new decision tree formulated not only with physicochemical parameters but also a biological parameter that was successfully used to screen estrogenic activities of the chemicals on amphibians. The biological parameter, CDOCKER interaction energy (Ebinding ) between chemicals and the target proteins was calculated based on the method of molecular docking, and it was used to revise the decision tree formulated by Hong only with physicochemical parameters for screening estrogenic activity of chemicals in rat. According to the correlation between Ebinding of rat and Xenopus laevis, a new decision tree for estrogenic activities in Xenopus laevis is finally proposed. Then it was validated by using the randomly 8 chemicals which can be frequently exposed to Xenopus laevis, and the agreement between the results from the new decision tree and the ones from experiments is generally satisfactory. Consequently, the new decision tree can be used to screen the estrogenic activities of the chemicals, and combinational use of the Ebinding and classical physicochemical parameters can greatly improves Hong's decision tree. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparing wavefront-optimized, wavefront-guided and topography-guided laser vision correction: clinical outcomes using an objective decision tree.

PubMed

Stonecipher, Karl; Parrish, Joseph; Stonecipher, Megan

2018-05-18

This review is intended to update and educate the reader on the currently available options for laser vision correction, more specifically, laser-assisted in-situ keratomileusis (LASIK). In addition, some related clinical outcomes data from over 1000 cases performed over a 1-year are presented to highlight some differences between the various treatment profiles currently available including the rapidity of visual recovery. The cases in question were performed on the basis of a decision tree to segregate patients on the basis of anatomical, topographic and aberrometry findings; the decision tree was formulated based on the data available in some of the reviewed articles. Numerous recent studies reported in the literature provide data related to the risks and benefits of LASIK; alternatives to a laser refractive procedure are also discussed. The results from these studies have been used to prepare a decision tree to assist the surgeon in choosing the best option for the patient based on the data from several standard preoperative diagnostic tests. The data presented here should aid surgeons in understanding the effects of currently available LASIK treatment profiles. Surgeons should also be able to appreciate how the findings were used to create a decision tree to help choose the most appropriate treatment profile for patients. Finally, the retrospective evaluation of clinical outcomes based on the decision tree should provide surgeons with a realistic expectation for their own outcomes should they adopt such a decision tree in their own practice.

Comparative analysis of tree classification models for detecting fusarium oxysporum f. sp cubense (TR4) based on multi soil sensor parameters

NASA Astrophysics Data System (ADS)

Estuar, Maria Regina Justina; Victorino, John Noel; Coronel, Andrei; Co, Jerelyn; Tiausas, Francis; Señires, Chiara Veronica

2017-09-01

Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.

Assessing electronic health record systems in emergency departments: Using a decision analytic Bayesian model.

PubMed

Ben-Assuli, Ofir; Leshno, Moshe

2016-09-01

In the last decade, health providers have implemented information systems to improve accuracy in medical diagnosis and decision-making. This article evaluates the impact of an electronic health record on emergency department physicians' diagnosis and admission decisions. A decision analytic approach using a decision tree was constructed to model the admission decision process to assess the added value of medical information retrieved from the electronic health record. Using a Bayesian statistical model, this method was evaluated on two coronary artery disease scenarios. The results show that the cases of coronary artery disease were better diagnosed when the electronic health record was consulted and led to more informed admission decisions. Furthermore, the value of medical information required for a specific admission decision in emergency departments could be quantified. The findings support the notion that physicians and patient healthcare can benefit from implementing electronic health record systems in emergency departments. © The Author(s) 2015.

GODDESS: A Goal-Directed Decision Structuring System.

DTIC Science & Technology

1980-06-01

differ- ent support techniques. From a practical viewpoint, though, the major drawback of manual interviews is their length and cost. Since real - time ...conducting his future inquiries. A direct man-machine interface could provide three distinct advantages. First, it offers the capability of real - time ...knowledge in tree form. In many real -world applications, the decision maker may not perceive a problem in the form of a time sequence of decision

A Decision-Tree Approach to Cost Comparison of Newborn Screening Strategies for Cystic Fibrosis

PubMed Central

Wells, Janelle; Rosenberg, Marjorie; Hoffman, Gary; Anstead, Michael

2012-01-01

OBJECTIVE: Because cystic fibrosis can be difficult to diagnose and treat early, newborn screening programs have rapidly developed nationwide but methods vary widely. We therefore investigated the costs and consequences or specific outcomes of the 2 most commonly used methods. METHODS: With available data on screening and follow-up, we used a simulation approach with decision trees to compare immunoreactive trypsinogen (IRT) screening followed by a second IRT test against an IRT/DNA analysis. By using a Monte Carlo simulation program, variation in the model parameters for counts at various nodes of the decision trees, as well as for costs, are included and applied to fictional cohorts of 100 000 newborns. The outcome measures included the numbers of newborns given a diagnosis of cystic fibrosis and costs of screening strategy at each branch and cost per newborn. RESULTS: Simulations revealed a substantial number of potential missed diagnoses for the IRT/IRT system versus IRT/DNA. Although the IRT/IRT strategy with commonly used cutoff values offers an average overall cost savings of $2.30 per newborn, a breakdown of costs by societal segments demonstrated higher out-of-pocket costs for families. Two potential system failures causing delayed diagnoses were identified relating to the screening protocols and the follow-up system. CONCLUSIONS: The IRT/IRT screening algorithm reduces the costs to laboratories and insurance companies but has more system failures. IRT/DNA offers other advantages, including fewer delayed diagnoses and lower out-of-pocket costs to families. PMID:22291119

Performance analysis of distributed applications using automatic classification of communication inefficiencies

DOEpatents

Vetter, Jeffrey S.

2005-02-01

The method and system described herein presents a technique for performance analysis that helps users understand the communication behavior of their message passing applications. The method and system described herein may automatically classifies individual communication operations and reveal the cause of communication inefficiencies in the application. This classification allows the developer to quickly focus on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, the method and system described herein trace the message operations of Message Passing Interface (MPI) applications and then classify each individual communication event using a supervised learning technique: decision tree classification. The decision tree may be trained using microbenchmarks that demonstrate both efficient and inefficient communication. Since the method and system described herein adapt to the target system's configuration through these microbenchmarks, they simultaneously automate the performance analysis process and improve classification accuracy. The method and system described herein may improve the accuracy of performance analysis and dramatically reduce the amount of data that users must encounter.

Lessons learned from Applications of a Decision Tree for Confronting Climate Change Uncertainty - the Short Term and the Long Term

NASA Astrophysics Data System (ADS)

Ray, P. A.; Wi, S.; Bonzanigo, L.; Taner, M. U.; Rodriguez, D.; Garcia, L.; Brown, C.

2016-12-01

The Decision Tree for Confronting Climate Change Uncertainty is a hierarchical, staged framework for accomplishing climate change risk management in water resources system investments. Since its development for the World Bank Water Group two years ago, the framework has been applied to pilot demonstration projects in Nepal (hydropower generation), Mexico (water supply), Kenya (multipurpose reservoir operation), and Indonesia (flood risks to dam infrastructure). An important finding of the Decision Tree demonstration projects has been the need to present the risks/opportunities of climate change to stakeholders and investors in proportion to risks/opportunities and hazards of other kinds. This presentation will provide an overview of tools and techniques used to quantify risks/opportunities to each of the project types listed above, with special attention to those found most useful for exploration of the risk space. Careful exploration of the risk/opportunity space shows that some interventions would be better taken now, whereas risks/opportunities of other types would be better instituted incrementally in order to maintain reversibility and flexibility. A number of factors contribute to the robustness/flexibility tradeoff: available capital, magnitude and imminence of potential risk/opportunity, modular (or not) character of investment, and risk aversion of the decision maker, among others. Finally, in each case, nuance was required in the translation of Decision Tree findings into actionable policy recommendations. Though the narrative of stakeholder solicitation, engagement, and ultimate partnership is unique to each case, summary lessons are available from the portfolio that can serve as a guideline to the community of climate change risk managers.

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

A Decision Tree for Nonmetric Sex Assessment from the Skull.

PubMed

Langley, Natalie R; Dudzik, Beatrix; Cloutier, Alesia

2018-01-01

This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits. © 2017 American Academy of Forensic Sciences.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Integrating Decision Tree and Hidden Markov Model (HMM) for Subtype Prediction of Human Influenza A Virus

NASA Astrophysics Data System (ADS)

Attaluri, Pavan K.; Chen, Zhengxin; Weerakoon, Aruna M.; Lu, Guoqing

Multiple criteria decision making (MCDM) has significant impact in bioinformatics. In the research reported here, we explore the integration of decision tree (DT) and Hidden Markov Model (HMM) for subtype prediction of human influenza A virus. Infection with influenza viruses continues to be an important public health problem. Viral strains of subtype H3N2 and H1N1 circulates in humans at least twice annually. The subtype detection depends mainly on the antigenic assay, which is time-consuming and not fully accurate. We have developed a Web system for accurate subtype detection of human influenza virus sequences. The preliminary experiment showed that this system is easy-to-use and powerful in identifying human influenza subtypes. Our next step is to examine the informative positions at the protein level and extend its current functionality to detect more subtypes. The web functions can be accessed at http://glee.ist.unomaha.edu/.

Learning accurate very fast decision trees from uncertain data streams

NASA Astrophysics Data System (ADS)

Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

2015-12-01

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

DTIC Science & Technology

2008-04-01

REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

PubMed

Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

2017-08-15

Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

Pruning a decision tree for selecting computer-related assistive devices for people with disabilities.

PubMed

Chi, Chia-Fen; Tseng, Li-Kai; Jang, Yuh

2012-07-01

Many disabled individuals lack extensive knowledge about assistive technology, which could help them use computers. In 1997, Denis Anson developed a decision tree of 49 evaluative questions designed to evaluate the functional capabilities of the disabled user and choose an appropriate combination of assistive devices, from a selection of 26, that enable the individual to use a computer. In general, occupational therapists guide the disabled users through this process. They often have to go over repetitive questions in order to find an appropriate device. A disabled user may require an alphanumeric entry device, a pointing device, an output device, a performance enhancement device, or some combination of these. Therefore, the current research eliminates redundant questions and divides Anson's decision tree into multiple independent subtrees to meet the actual demand of computer users with disabilities. The modified decision tree was tested by six disabled users to prove it can determine a complete set of assistive devices with a smaller number of evaluative questions. The means to insert new categories of computer-related assistive devices was included to ensure the decision tree can be expanded and updated. The current decision tree can help the disabled users and assistive technology practitioners to find appropriate computer-related assistive devices that meet with clients' individual needs in an efficient manner.

Uncertain decision tree inductive inference

NASA Astrophysics Data System (ADS)

Zarban, L.; Jafari, S.; Fakhrahmad, S. M.

2011-10-01

Induction is the process of reasoning in which general rules are formulated based on limited observations of recurring phenomenal patterns. Decision tree learning is one of the most widely used and practical inductive methods, which represents the results in a tree scheme. Various decision tree algorithms have already been proposed such as CLS, ID3, Assistant C4.5, REPTree and Random Tree. These algorithms suffer from some major shortcomings. In this article, after discussing the main limitations of the existing methods, we introduce a new decision tree induction algorithm, which overcomes all the problems existing in its counterparts. The new method uses bit strings and maintains important information on them. This use of bit strings and logical operation on them causes high speed during the induction process. Therefore, it has several important features: it deals with inconsistencies in data, avoids overfitting and handles uncertainty. We also illustrate more advantages and the new features of the proposed method. The experimental results show the effectiveness of the method in comparison with other methods existing in the literature.

Visualizing speciation in artificial cichlid fish.

PubMed

Clement, Ross

2006-01-01

The Cichlid Speciation Project (CSP) is an ALife simulation system for investigating open problems in the speciation of African cichlid fish. The CSP can be used to perform a wide range of experiments that show that speciation is a natural consequence of certain biological systems. A visualization system capable of extracting the history of speciation from low-level trace data and creating a phylogenetic tree has been implemented. Unlike previous approaches, this visualization system presents a concrete trace of speciation, rather than a summary of low-level information from which the viewer can make subjective decisions on how speciation progressed. The phylogenetic trees are a more objective visualization of speciation, and enable automated collection and summarization of the results of experiments. The visualization system is used to create a phylogenetic tree from an experiment that models sympatric speciation.

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

Comparative Issues and Methods in Organizational Diagnosis. Report II. The Decision Tree Approach.

DTIC Science & Technology

organizational diagnosis . The advantages and disadvantages of the decision-tree approach generally, and in this study specifically, are examined. A pre-test, using a civilian sample of 174 work groups with Survey of Organizations data, was conducted to assess various decision-tree classification criteria, in terms of their similarity to the distance function used by Bowers and Hausser (1977). The results suggested the use of a large developmental sample, which should result in more distinctly defined boundary lines between classification profiles. Also, the decision matrix

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.

PubMed

Durham, Erin-Elizabeth A; Yu, Xiaxia; Harrison, Robert W

2014-12-01

Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.

MRI-based decision tree model for diagnosis of biliary atresia.

PubMed

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

PubMed

Satomi, Junichiro; Ghaibeh, A Ammar; Moriguchi, Hiroki; Nagahiro, Shinji

2015-07-01

The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision tree's accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

PubMed

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.

PubMed

Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin

2017-08-16

The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.

Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation.

PubMed

Shao, Q; Rowe, R C; York, P

2007-06-01

Understanding of the cause-effect relationships between formulation ingredients, process conditions and product properties is essential for developing a quality product. However, the formulation knowledge is often hidden in experimental data and not easily interpretable. This study compares neurofuzzy logic and decision tree approaches in discovering hidden knowledge from an immediate release tablet formulation database relating formulation ingredients (silica aerogel, magnesium stearate, microcrystalline cellulose and sodium carboxymethylcellulose) and process variables (dwell time and compression force) to tablet properties (tensile strength, disintegration time, friability, capping and drug dissolution at various time intervals). Both approaches successfully generated useful knowledge in the form of either "if then" rules or decision trees. Although different strategies are employed by the two approaches in generating rules/trees, similar knowledge was discovered in most cases. However, as decision trees are not able to deal with continuous dependent variables, data discretisation procedures are generally required.

Analysis of driver merging behavior at lane drops on freeways.

DOT National Transportation Integrated Search

2013-12-01

Lane changing assistance systems advise drivers on safe gaps for making mandatory lane changes at lane drops. In this : study, such a system was developed using a Bayes classifier and a decision tree to model lane changes. Detailed vehicle : trajecto...

Formal analysis of the surgical pathway and development of a new software tool to assist surgeons in the decision making in primary breast surgery.

PubMed

Catanuto, Giuseppe; Pappalardo, Francesco; Rocco, Nicola; Leotta, Marco; Ursino, Venera; Chiodini, Paolo; Buggi, Federico; Folli, Secondo; Catalano, Francesca; Nava, Maurizio B

2016-10-01

The increased complexity of the decisional process in breast cancer surgery is well documented. With this study we aimed to create a software tool able to assist patients and surgeons in taking proper decisions. We hypothesized that the endpoints of breast cancer surgery could be addressed combining a set of decisional drivers. We created a decision support system software tool (DSS) and an interactive decision tree. A formal analysis estimated the information gain derived from each feature in the process. We tested the DSS on 52 patients and we analyzed the concordance of decisions obtained by different users and between the DSS suggestions and the actual surgery. We also tested the ability of the system to prevent post breast conservation deformities. The information gain revealed that patients preferences are the root of our decision tree. An observed concordance respectively of 0.98 and 0.88 was reported when the DSS was used twice by an expert operator or by a newly trained operator vs. an expert one. The observed concordance between the DSS suggestion and the actual decision was 0.69. A significantly higher incidence of post breast conservation defects was reported among patients who did not follow the DSS decision (Type III of Fitoussi, N = 4; 33.3%, p = 0.004). The DSS decisions can be reproduced by operators with different experience. The concordance between suggestions and actual decision is quite low, however the DSS is able to prevent post- breast conservation deformities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Generation and Termination of Binary Decision Trees for Nonparametric Multiclass Classification.

DTIC Science & Technology

1984-10-01

O M coF=F;; UMBER2. GOVT ACCE5SION NO.1 3 . REC,PINS :A7AL:,G NUMBER ( ’eneration and Terminat_,on :)f Binary D-ecision jC j ik; Trees for Nonnararetrc...1-I . v)IAMO 0~I4 EDvt" O F I 00 . 3 15I OR%.OL.ETL - S-S OCTOBER 1984 LIDS-P-1411 GENERATION AND TERMINATION OF BINARY DECISION TREES FOR...minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively. 0 0 0/ 6 0¢ A 3 I. Introduction We state

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

The Decision Tree for Teaching Management of Uncertainty

ERIC Educational Resources Information Center

Knaggs, Sara J.; And Others

1974-01-01

A 'decision tree' consists of an outline of the patient's symptoms and a logic for decision and action. It is felt that this approach to the decisionmaking process better facilitates each learner's application of his own level of knowledge and skills. (Author)

Cost-effectiveness Analysis with Influence Diagrams.

PubMed

Arias, M; Díez, F J

2015-01-01

Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEA for very small problems. To develop a method for CEA in problems involving several dozen variables. We explain how to build influence diagrams (IDs) that explicitly represent cost and effectiveness. We propose an algorithm for evaluating cost-effectiveness IDs directly, i.e., without expanding an equivalent decision tree. The evaluation of an ID returns a set of intervals for the willingness to pay - separated by cost-effectiveness thresholds - and, for each interval, the cost, the effectiveness, and the optimal intervention. The algorithm that evaluates the ID directly is in general much more efficient than the brute-force method, which is in turn more efficient than the expansion of an equivalent decision tree. Using OpenMarkov, an open-source software tool that implements this algorithm, we have been able to perform CEAs on several IDs whose equivalent decision trees contain millions of branches. IDs can perform CEA on large problems that cannot be analyzed with decision trees.

A multistage decision support framework to guide tree species management under climate change via habitat suitability and colonization models, and a knowledge-based scoring system

Treesearch

Anantha M. Prasad; Louis R. Iverson; Stephen N. Matthews; Matthew P. Peters

2016-01-01

Context. No single model can capture the complex species range dynamics under changing climates--hence the need for a combination approach that addresses management concerns. Objective. A multistage approach is illustrated to manage forested landscapes under climate change. We combine a tree species habitat model--DISTRIB II, a species colonization model--SHIFT, and...

Automated Decision Tree Classification of Corneal Shape

PubMed Central

Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

2011-01-01

Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems. PMID:16357645

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

PubMed

Surucu, Murat; Shah, Karan K; Mescioglu, Ibrahim; Roeske, John C; Small, William; Choi, Mehee; Emami, Bahman

2016-02-01

To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources. © The Author(s) 2015.

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, P.; Beaudet, P.

1980-01-01

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Decision-Tree Analysis for Predicting First-Time Pass/Fail Rates for the NCLEX-RN® in Associate Degree Nursing Students.

PubMed

Chen, Hsiu-Chin; Bennett, Sean

2016-08-01

Little evidence shows the use of decision-tree algorithms in identifying predictors and analyzing their associations with pass rates for the NCLEX-RN(®) in associate degree nursing students. This longitudinal and retrospective cohort study investigated whether a decision-tree algorithm could be used to develop an accurate prediction model for the students' passing or failing the NCLEX-RN. This study used archived data from 453 associate degree nursing students in a selected program. The chi-squared automatic interaction detection analysis of the decision trees module was used to examine the effect of the collected predictors on passing/failing the NCLEX-RN. The actual percentage scores of Assessment Technologies Institute®'s RN Comprehensive Predictor(®) accurately identified students at risk of failing. The classification model correctly classified 92.7% of the students for passing. This study applied the decision-tree model to analyze a sequence database for developing a prediction model for early remediation in preparation for the NCLEXRN. [J Nurs Educ. 2016;55(8):454-457.]. Copyright 2016, SLACK Incorporated.

Multi-agent simulation of generation expansion in electricity markets.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Botterud, A; Mahalik, M. R.; Veselka, T. D.

2007-06-01

We present a new multi-agent model of generation expansion in electricity markets. The model simulates generation investment decisions of decentralized generating companies (GenCos) interacting in a complex, multidimensional environment. A probabilistic dispatch algorithm calculates prices and profits for new candidate units in different future states of the system. Uncertainties in future load, hydropower conditions, and competitors actions are represented in a scenario tree, and decision analysis is used to identify the optimal expansion decision for each individual GenCo. We test the model using real data for the Korea power system under different assumptions about market design, market concentration, and GenCo'smore » assumed expectations about their competitors investment decisions.« less

A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

PubMed Central

Sánchez-Rodríguez, David; Hernández-Morera, Pablo; Quinteiro, José Ma.; Alonso-González, Itziar

2015-01-01

Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have. PMID:26110413

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

PubMed

Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

2017-10-11

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

RE-Powering’s Electronic Decision Tree

EPA Pesticide Factsheets

Developed by US EPA's RE-Powering America's Land Initiative, the RE-Powering Decision Trees tool guides interested parties through a process to screen sites for their suitability for solar photovoltaics or wind installations

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

NASA Technical Reports Server (NTRS)

Wang, Yao Xun

2011-01-01

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

Decision tree analysis as a supplementary tool to enhance histomorphological differentiation when distinguishing human from non-human cranial bone in both burnt and unburnt states: A feasibility study.

PubMed

Simmons, T; Goodburn, B; Singhrao, S K

2016-01-01

This feasibility study was undertaken to describe and record the histological characteristics of burnt and unburnt cranial bone fragments from human and non-human bones. Reference series of fully mineralized, transverse sections of cranial bone, from all variables and specimen states, were prepared by manual cutting and semi-automated grinding and polishing methods. A photomicrograph catalogue reflecting differences in burnt and unburnt bone from human and non-humans was recorded and qualitative analysis was performed using an established classification system based on primary bone characteristics. The histomorphology associated with human and non-human samples was, for the main part, preserved following burning at high temperature. Clearly, fibro-lamellar complex tissue subtypes, such as plexiform or laminar primary bone, were only present in non-human bones. A decision tree analysis based on histological features provided a definitive identification key for distinguishing human from non-human bone, with an accuracy of 100%. The decision tree for samples where burning was unknown was 96% accurate, and multi-step classification to taxon was possible with 100% accuracy. The results of this feasibility study strongly suggest that histology remains a viable alternative technique if fragments of cranial bone require forensic examination in both burnt and unburnt states. The decision tree analysis may provide an additional but vital tool to enhance data interpretation. Further studies are needed to assess variation in histomorphology taking into account other cranial bones, ontogeny, species and burning conditions. © The Author(s) 2015.

Decision Tree Approach for Soil Liquefaction Assessment

PubMed Central

Gandomi, Amir H.; Fridline, Mark M.; Roke, David A.

2013-01-01

In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view. PMID:24489498

Decision tree approach for soil liquefaction assessment.

PubMed

Gandomi, Amir H; Fridline, Mark M; Roke, David A

2013-01-01

In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view.

Fast Image Texture Classification Using Decision Trees

NASA Technical Reports Server (NTRS)

Thompson, David R.

2011-01-01

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

An Exploratory Data Analysis System for Support in Medical Decision-Making

PubMed Central

Copeland, J. A.; Hamel, B.; Bourne, J. R.

1979-01-01

An experimental system was developed to allow retrieval and analysis of data collected during a study of neurobehavioral correlates of renal disease. After retrieving data organized in a relational data base, simple bivariate statistics of parametric and nonparametric nature could be conducted. An “exploratory” mode in which the system provided guidance in selection of appropriate statistical analyses was also available to the user. The system traversed a decision tree using the inherent qualities of the data (e.g., the identity and number of patients, tests, and time epochs) to search for the appropriate analyses to employ.

Use of graphics in decision aids for telerobotic control: (Parts 5-8 of an 8-part MIT progress report)

NASA Technical Reports Server (NTRS)

Sheridan, Thomas B.; Roseborough, James B.; Das, Hari; Chin, Kan-Ping; Inoue, Seiichi

1989-01-01

Four separate projects recently completed or in progress at the MIT Man-Machine Systems Laboratory are summarized. They are: a decision aid for retrieving a tumbling satellite in space; kinematic control and graphic display of redundant teleoperators; real time terrain/object generation: a quad-tree approach; and two dimensional control for three dimensional obstacle avoidance.

Ethnographic Decision Tree Modeling: A Research Method for Counseling Psychology.

ERIC Educational Resources Information Center

Beck, Kirk A.

2005-01-01

This article describes ethnographic decision tree modeling (EDTM; C. H. Gladwin, 1989) as a mixed method design appropriate for counseling psychology research. EDTM is introduced and located within a postpositivist research paradigm. Decision theory that informs EDTM is reviewed, and the 2 phases of EDTM are highlighted. The 1st phase, model…

Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System

NASA Astrophysics Data System (ADS)

Wang, Hongcui; Kawahara, Tatsuya

CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

PubMed

Bianne-Bernard, Anne-Laure; Menasri, Farès; Al-Hajj Mohamad, Rami; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

2011-10-01

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

Lessons Learned from Applications of a Climate Change Decision Tree toWater System Projects in Kenya and Nepal

NASA Astrophysics Data System (ADS)

Ray, P. A.; Bonzanigo, L.; Taner, M. U.; Wi, S.; Yang, Y. C. E.; Brown, C.

2015-12-01

The Decision Tree Framework developed for the World Bank's Water Partnership Program provides resource-limited project planners and program managers with a cost-effective and effort-efficient, scientifically defensible, repeatable, and clear method for demonstrating the robustness of a project to climate change. At the conclusion of this process, the project planner is empowered to confidently communicate the method by which the vulnerabilities of the project have been assessed, and how the adjustments that were made (if any were necessary) improved the project's feasibility and profitability. The framework adopts a "bottom-up" approach to risk assessment that aims at a thorough understanding of a project's vulnerabilities to climate change in the context of other nonclimate uncertainties (e.g., economic, environmental, demographic, political). It helps identify projects that perform well across a wide range of potential future climate conditions, as opposed to seeking solutions that are optimal in expected conditions but fragile to conditions deviating from the expected. Lessons learned through application of the Decision Tree to case studies in Kenya and Nepal will be presented, and aspects of the framework requiring further refinement will be described.

A fuzzy decision tree for fault classification.

PubMed

Zio, Enrico; Baraldi, Piero; Popescu, Irina C

2008-02-01

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.

The Potential of GIS as a Management Tool for Avenue Trees Population in Small Communities; a Case Study of Idi-Shin Community, Ibadan, Nigeria

NASA Astrophysics Data System (ADS)

Olokeogun, O. S.; Akintola, O. O.; Abodunrin, E. K.

2016-06-01

This study demonstrates the potentials of Geographic Information System (GIS) as a management tool for avenue trees (Street trees) populations in small communities (using Idi-Ishin community, Ibadan, Nigeria as a case study). GIS is a decision support system which integrate data or set of data from different sources, bringing them under the same referencing system in a computer system. An Ikonos Imagery (1m Spatial Resolution) of the study area was digitized to produce a digital map using ArcGIS 10.1 version. The avenue trees species ≥ 5cm diameter at breast height (DBH) was selected for enumeration. These trees were then measured and tagged. The Height, Girth and Geographic location (X &Y coordinate) of the trees were measured with Haga altimeter, Girthing tape and Hand held Global Positioning System (GPS) respectively. The species and families of the trees enumerated were also identified. Data were analysed for basal area (BA) and volume (V). A total number of 43 avenue trees were assessed in Idi-Ishin Community. Roystonea regia accounted for the majority of the avenue trees (25.58%), followed by Polyanthia longiflora (23.26%), Gliricida seprium (20.93%), Eucalyptus toreliana (13.95%), Delunix regea (6.98%). However Terminalia catapa, Terminalia radii, Azadrachita indica and Newbodia levis had the same abundance of 2.33%. It was also observed that the benefits derived from these avenue trees includes; Carbon sequestration, Beautification, Wind break and shade. A spatial relational database was created for the assessed avenue trees using ArcCatalog of ArcGIS 10.1 version. Based on the findings from the study (which serves as baseline information for the management of the avenue trees in the study area), it was therefore recommended that subsequent assessment should be carried out at 3-5 year interval in other to ensure proper and continuous monitoring and updating of the data.

KIPSE1: A Knowledge-based Interactive Problem Solving Environment for data estimation and pattern classification

NASA Technical Reports Server (NTRS)

Han, Chia Yung; Wan, Liqun; Wee, William G.

1990-01-01

A knowledge-based interactive problem solving environment called KIPSE1 is presented. The KIPSE1 is a system built on a commercial expert system shell, the KEE system. This environment gives user capability to carry out exploratory data analysis and pattern classification tasks. A good solution often consists of a sequence of steps with a set of methods used at each step. In KIPSE1, solution is represented in the form of a decision tree and each node of the solution tree represents a partial solution to the problem. Many methodologies are provided at each node to the user such that the user can interactively select the method and data sets to test and subsequently examine the results. Otherwise, users are allowed to make decisions at various stages of problem solving to subdivide the problem into smaller subproblems such that a large problem can be handled and a better solution can be found.

PRIA 3 Fee Determination Decision Tree

EPA Pesticide Factsheets

The PRIA 3 decision tree will help applicants requesting a pesticide registration or certain tolerance action to accurately identify the category of their application and the amount of the required fee before they submit the application.

Solar and Wind Site Screening Decision Trees

EPA Pesticide Factsheets

EPA and NREL created a decision tree to guide state and local governments and other stakeholders through a process for screening sites for their suitability for future redevelopment with solar photovoltaic (PV) energy and wind energy.

Electricity system expansion studies to consider uncertainties and interactions in restructured markets

NASA Astrophysics Data System (ADS)

Jin, Shan

This dissertation concerns power system expansion planning under different market mechanisms. The thesis follows a three paper format, in which each paper emphasizes a different perspective. The first paper investigates the impact of market uncertainties on a long term centralized generation expansion planning problem. The problem is modeled as a two-stage stochastic program with uncertain fuel prices and demands, which are represented as probabilistic scenario paths in a multi-period tree. Two measurements, expected cost (EC) and Conditional Value-at-Risk (CVaR), are used to minimize, respectively, the total expected cost among scenarios and the risk of incurring high costs in unfavorable scenarios. We sample paths from the scenario tree to reduce the problem scale and determine the sufficient number of scenarios by computing confidence intervals on the objective values. The second paper studies an integrated electricity supply system including generation, transmission and fuel transportation with a restructured wholesale electricity market. This integrated system expansion problem is modeled as a bi-level program in which a centralized system expansion decision is made in the upper level and the operational decisions of multiple market participants are made in the lower level. The difficulty of solving a bi-level programming problem to global optimality is discussed and three problem relaxations obtained by reformulation are explored. The third paper solves a more realistic market-based generation and transmission expansion problem. It focuses on interactions among a centralized transmission expansion decision and decentralized generation expansion decisions. It allows each generator to make its own strategic investment and operational decisions both in response to a transmission expansion decision and in anticipation of a market price settled by an Independent System Operator (ISO) market clearing problem. The model poses a complicated tri-level structure including an equilibrium problem with equilibrium constraints (EPEC) sub-problem. A hybrid iterative algorithm is proposed to solve the problem efficiently and reliably.

Using multiobjective tradeoff sets and Multivariate Regression Trees to identify critical and robust decisions for long term water utility planning

NASA Astrophysics Data System (ADS)

Smith, R.; Kasprzyk, J. R.; Balaji, R.

2017-12-01

In light of deeply uncertain factors like future climate change and population shifts, responsible resource management will require new types of information and strategies. For water utilities, this entails potential expansion and efficient management of water supply infrastructure systems for changes in overall supply; changes in frequency and severity of climate extremes such as droughts and floods; and variable demands, all while accounting for conflicting long and short term performance objectives. Multiobjective Evolutionary Algorithms (MOEAs) are emerging decision support tools that have been used by researchers and, more recently, water utilities to efficiently generate and evaluate thousands of planning portfolios. The tradeoffs between conflicting objectives are explored in an automated way to produce (often large) suites of portfolios that strike different balances of performance. Once generated, the sets of optimized portfolios are used to support relatively subjective assertions of priorities and human reasoning, leading to adoption of a plan. These large tradeoff sets contain information about complex relationships between decisions and between groups of decisions and performance that, until now, has not been quantitatively described. We present a novel use of Multivariate Regression Trees (MRTs) to analyze tradeoff sets to reveal these relationships and critical decisions. Additionally, when MRTs are applied to tradeoff sets developed for different realizations of an uncertain future, they can identify decisions that are robust across a wide range of conditions and produce fundamental insights about the system being optimized.

Plant Intellectual Property Transfer Mechanisms at US Universities.

ERIC Educational Resources Information Center

Price, Steven C.; Renk, Bryan Z.

2000-01-01

U.S. colleges of agriculture and technology transfer offices have historically been in conflict over the management of plant varieties. A simple model that would allow these competing systems to become integrated uses a decision tree. (Author/JOW)

Comparison of a classical with a highly formularized body condition scoring system for dairy cattle.

PubMed

Isensee, A; Leiber, F; Bieber, A; Spengler, A; Ivemeyer, S; Maurer, V; Klocke, P

2014-12-01

Body condition scoring is a common tool to assess the subcutaneous fat reserves of dairy cows. Because of its subjectivity, which causes limits in repeatability, it is often discussed controversially. Aim of the current study was to evaluate the impact of considering the cows overall appearance on the scoring process and on the validity of the results. Therefore, two different methods to reveal body condition scores (BCS), 'independent BCS' (iBCS) and 'dependent BCS' (dBCS), were used to assess 1111 Swiss Brown Cattle. The iBCS and the dBCS systems were both working with the same flowchart with a decision tree structure for visual and palpatory assessment using a scale from 2 to 5 with increment units of 0.25. The iBCS was created strictly complying with the defined frames of the decision tree structure. The system was chosen due to its formularized approach to reduce the influence of subjective impressions. By contrast, the dBCS system, which was in line with common practice, had a more open approach, where - besides the decision tree - the overall impression of the cow's physical appearance was taken into account for generating the final score. Ultrasound measurement of the back fat thickness (BFT) was applied as a validation method. The dBCS turned out to be the better predictor of BFT, explaining 67.3% of the variance. The iBCS was only able to explain 47.3% of the BFT variance. Within the whole data set, only 31.3% of the animals received identical dBCS and iBCS. The pin bone region caused the most deviations between dBCS and iBCS, but also assessing the pelvis line, the hook bones and the ligaments led to divergences in around 20% of the scored animals. The study showed that during the assessment of body condition a strict adherence to a decision tree is a possible source of inexact classifications. Some body regions, especially the pin bones, proved to be particularly challenging for scoring due to difficulties in assessing them. All the more, the inclusion of the overall appearance of the cow into the assessment process counteracted these errors and led to a fair predictability of BFT with the flowchart-based BCS. This might be particularly important, if different cattle types and breeds are assessed.

A New Decision Tree to Solve the Puzzle of Alzheimer's Disease Pathogenesis Through Standard Diagnosis Scoring System.

PubMed

Kumar, Ashwani; Singh, Tiratha Raj

2017-03-01

Alzheimer's disease (AD) is a progressive, incurable and terminal neurodegenerative disorder of the brain and is associated with mutations in amyloid precursor protein, presenilin 1, presenilin 2 or apolipoprotein E, but its underlying mechanisms are still not fully understood. Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the clinical dataset are therefore increasingly becoming necessary. The current study deals with the construction of classifiers that can be human readable as well as robust in performance for gene dataset of AD using a decision tree. Models of classification for different AD genes were generated according to Mini-Mental State Examination scores and all other vital parameters to achieve the identification of the expression level of different proteins of disorder that may possibly determine the involvement of genes in various AD pathogenesis pathways. The effectiveness of decision tree in AD diagnosis is determined by information gain with confidence value (0.96), specificity (92 %), sensitivity (98 %) and accuracy (77 %). Besides this functional gene classification using different parameters and enrichment analysis, our finding indicates that the measures of all the gene assess in single cohorts are sufficient to diagnose AD and will help in the prediction of important parameters for other relevant assessments.

Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities.

PubMed

Moon, Mikyung; Lee, Soo-Kyoung

2017-01-01

The purpose of this study was to use decision tree analysis to explore the factors associated with pressure ulcers (PUs) among elderly people admitted to Korean long-term care facilities. The data were extracted from the 2014 National Inpatient Sample (NIS)-data of Health Insurance Review and Assessment Service (HIRA). A MapReduce-based program was implemented to join and filter 5 tables of the NIS. The outcome predicted by the decision tree model was the prevalence of PUs as defined by the Korean Standard Classification of Disease-7 (KCD-7; code L89 * ). Using R 3.3.1, a decision tree was generated with the finalized 15,856 cases and 830 variables. The decision tree displayed 15 subgroups with 8 variables showing 0.804 accuracy, 0.820 sensitivity, and 0.787 specificity. The most significant primary predictor of PUs was length of stay less than 0.5 day. Other predictors were the presence of an infectious wound dressing, followed by having diagnoses numbering less than 3.5 and the presence of a simple dressing. Among diagnoses, "injuries to the hip and thigh" was the top predictor ranking 5th overall. Total hospital cost exceeding 2,200,000 Korean won (US $2,000) rounded out the top 7. These results support previous studies that showed length of stay, comorbidity, and total hospital cost were associated with PUs. Moreover, wound dressings were commonly used to treat PUs. They also show that machine learning, such as a decision tree, could effectively predict PUs using big data.

Predicting the probability of mortality of gastric cancer patients using decision tree.

PubMed

Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

2015-06-01

Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

GIS-based integrated assessment and decision support system for land use planning in consideration of carbon sequestration benefits

NASA Astrophysics Data System (ADS)

Wang, Jun; Chen, J. M.; Li, Manchun; Ju, Weimin

2007-06-01

As the major eligible land use activities in the Clean Development Mechanism (CDM), afforestation and reforestation offer opportunities and potential economic benefits for developing countries to participate in carbon-trade in the potential international carbon (C) sink markets. However, the design and selection of appropriate afforestation and reforestation locations in CDM are complex processes which need integrated assessment (IA) of C sequestration (CS) potential, environmental effects, and socio-economic impacts. This paper promotes the consideration of CS benefits in local land use planning and presents a GIS-based integrated assessment and spatial decision support system (IA-SDSS) to support decision-making on 'where' and 'how' to afforest. It integrates an Integrated Terrestrial Ecosystem Carbon Model (InTEC) and a GIS platform for modeling regional long-term CS potential and assessment of geo-referenced land use criteria including CS consequence, and produces ranking of plantation schemes with different tree species using the Analytic hierarchy process (AHP) method. Three land use scenarios are investigated: (i) traditional land use planning criteria without C benefits, (ii) land use for CS with low C price, and (iii) land use for CS with high price. Different scenarios and consequences will influence the weights of tree-species selection in the AHP decision process.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

PubMed

Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme A.; Oliveira, Alexandre C.; Cooper, Gregory F.

2015-01-01

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. PMID:26098570

Systematic methods for knowledge acquisition and expert system development

NASA Technical Reports Server (NTRS)

Belkin, Brenda L.; Stengel, Robert F.

1991-01-01

Nine cooperating rule-based systems, collectively called AUTOCREW which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy INS was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. ANOVA results show that statistically different position accuracies are obtained when different navaids are used, the number of navaids aiding the INS is varied, the aircraft's trajectory is varied, and the performance history is varied. The ID3 algorithm determines the NSM expert's classification rules in the form of decision trees. The performance of these decision trees was assessed on two arbitrary trajectories, and the results demonstrate that the NSM expert adapts to new situations and provides reasonable estimates of the expected hybrid performance.

Space/age forestry: Implications of planting density and rotation age in SRIC management decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merriam, R.A.; Phillips, V.D.; Liu, W.

1993-12-31

Short-rotation intensive-culture (SRIC) of promising tree crops is being evaluated worldwide for the production of methanol, ethanol, and electricity from renewable biomass resources. Planting density and rotation age are fundamental management decisions associated with SRIC energy plantations. Most studies of these variables have been conducted without the benefit of a unifying theory of the effects of growing space and rotation age on individual tree growth and stand level productivity. A modeling procedure based on field trials of Eucalyptus spp. is presented that evaluates the growth potential of a tree in the absence and presence of competition of neighboring trees inmore » a stand. The results of this analysis are useful in clarifying economic implications of different growing space and rotation age decisions that tree plantation managers must make. The procedure is readily applicable to other species under consideration for SRIC plantations at any location.« less

A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities

USGS Publications Warehouse

Capel, Paul D.; Wolock, David M.; Coupe, Richard H.; Roth, Jason L.

2018-01-10

Agricultural activities can affect water quality and the health of aquatic ecosystems; many water-quality issues originate with the movement of water, agricultural chemicals, and eroded soil from agricultural areas to streams and groundwater. Most agricultural activities are designed to sustain or increase crop production, while some are designed to protect soil and water resources. Numerous soil- and water-protection practices are designed to reduce the volume and velocity of runoff and increase infiltration. This report presents a conceptual framework that combines generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities, as they relate to hydrology, to create attainable expectations for the protection of—with the goal of improving—water quality through changes in an agricultural activity.The framework presented uses two types of decision trees to guide decision making toward attainable expectations regarding the effectiveness of changing agricultural activities to protect and improve water quality in streams. One decision tree organizes decision making by considering the hydrologic setting and chemical behaviors, largely at the field scale. This decision tree can help determine which agricultural activities could effectively protect and improve water quality in a stream from the movement of chemicals, or sediment, from a field. The second decision tree is a chemical fate accounting tree. This decision tree helps set attainable expectations for the permanent removal of sediment, elements, and organic chemicals—such as herbicides and insecticides—through trapping or conservation tillage practices. Collectively, this conceptual framework consolidates diverse hydrologic settings, chemicals, and agricultural activities into a single, broad context that can be used to set attainable expectations for agricultural activities. This framework also enables better decision making for future agricultural activities as a means to reduce current, and prevent new, water-quality issues.

Vlsi implementation of flexible architecture for decision tree classification in data mining

NASA Astrophysics Data System (ADS)

Sharma, K. Venkatesh; Shewandagn, Behailu; Bhukya, Shankar Nayak

2017-07-01

The Data mining algorithms have become vital to researchers in science, engineering, medicine, business, search and security domains. In recent years, there has been a terrific raise in the size of the data being collected and analyzed. Classification is the main difficulty faced in data mining. In a number of the solutions developed for this problem, most accepted one is Decision Tree Classification (DTC) that gives high precision while handling very large amount of data. This paper presents VLSI implementation of flexible architecture for Decision Tree classification in data mining using c4.5 algorithm.

Risk-Based Prioritization of Research for Aviation Security Using Logic-Evolved Decision Analysis

NASA Technical Reports Server (NTRS)

Eisenhawer, S. W.; Bott, T. F.; Sorokach, M. R.; Jones, F. P.; Foggia, J. R.

2004-01-01

The National Aeronautics and Space Administration is developing advanced technologies to reduce terrorist risk for the air transportation system. Decision support tools are needed to help allocate assets to the most promising research. An approach to rank ordering technologies (using logic-evolved decision analysis), with risk reduction as the metric, is presented. The development of a spanning set of scenarios using a logic-gate tree is described. Baseline risk for these scenarios is evaluated with an approximate reasoning model. Illustrative risk and risk reduction results are presented.

Applying Data Mining Techniques to Extract Hidden Patterns about Breast Cancer Survival in an Iranian Cohort Study.

PubMed

Khalkhali, Hamid Reza; Lotfnezhad Afshar, Hadi; Esnaashari, Omid; Jabbari, Nasrollah

2016-01-01

Breast cancer survival has been analyzed by many standard data mining algorithms. A group of these algorithms belonged to the decision tree category. Ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied already. The classification and regression trees (CART) was applied to a breast cancer database contained information on 569 patients in 2007-2010. The measurement of Gini impurity used for categorical target variables was utilized. The classification error that is a function of tree size was measured by 10-fold cross-validation experiments. The performance of created model was evaluated by the criteria as accuracy, sensitivity and specificity. The CART model produced a decision tree with 17 nodes, 9 of which were associated with a set of rules. The rules were meaningful clinically. They showed in the if-then format that Stage was the most important variable for predicting breast cancer survival. The scores of accuracy, sensitivity and specificity were: 80.3%, 93.5% and 53%, respectively. The current study model as the first one created by the CART was able to extract useful hidden rules from a relatively small size dataset.

The Utility of Decision Trees in Oncofertility Care in Japan.

PubMed

Ito, Yuki; Shiraishi, Eriko; Kato, Atsuko; Haino, Takayuki; Sugimoto, Kouhei; Okamoto, Aikou; Suzuki, Nao

2017-03-01

To identify the utility and issues associated with the use of decision trees in oncofertility patient care in Japan. A total of 35 women who had been diagnosed with cancer, but had not begun anticancer treatment, were enrolled. We applied the oncofertility decision tree for women published by Gardino et al. to counsel a consecutive series of women on fertility preservation (FP) options following cancer diagnosis. Percentage of women who decided to undergo oocyte retrieval for embryo cryopreservation and the expected live-birth rate for these patients were calculated using the following equation: expected live-birth rate = pregnancy rate at each age per embryo transfer × (1 - miscarriage rate) × No. of cryopreserved embryos. Oocyte retrieval was performed for 17 patients (48.6%; mean ± standard deviation [SD] age, 36.35 ± 3.82 years). The mean ± SD number of cryopreserved embryos was 5.29 ± 4.63. The expected live-birth rate was 0.66. The expected live-birth rate with FP indicated that one in three oncofertility patients would not expect to have a live birth following oocyte retrieval and embryo cryopreservation. While the decision trees were useful as decision-making tools for women contemplating FP, in the context of the current restrictions on oocyte donation and the extremely small number of adoptions in Japan, the remaining options for fertility after cancer are limited. In order for cancer survivors to feel secure in their decisions, the decision tree may need to be adapted simultaneously with improvements to the social environment, such as greater support for adoption.

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand.

PubMed

Kurzynski, Marek; Krysmann, Maciej; Trajdos, Pawel; Wolczowski, Andrzej

2016-02-01

In this paper the problem of recognition of the intended hand movements for the control of bio-prosthetic hand is addressed. The proposed method is based on recognition of electromiographic (EMG) and mechanomiographic (MMG) biosignals using a multiclassifier system (MCS) working in a two-level structure with a dynamic ensemble selection (DES) scheme and original concepts of competence function. Additionally, feedback information coming from bioprosthesis sensors on the correct/incorrect classification is applied to the adjustment of the combining mechanism during MCS operation through adaptive tuning competences of base classifiers depending on their decisions. Three MCS systems operating in decision tree structure and with different tuning algorithms are developed. In the MCS1 system, competence is uniformly allocated to each class belonging to the group indicated by the feedback signal. In the MCS2 system, the modification of competence depends on the node of decision tree at which a correct/incorrect classification is made. In the MCS3 system, the randomized model of classifier and the concept of cross-competence are used in the tuning procedure. Experimental investigations on the real data and computer-simulated procedure of generating feedback signals are performed. In these investigations classification accuracy of the MCS systems developed is compared and furthermore, the MCS systems are evaluated with respect to the effectiveness of the procedure of tuning competence. The results obtained indicate that modification of competence of base classifiers during the working phase essentially improves performance of the MCS system and that this improvement depends on the MCS system and tuning method used. Copyright © 2015 Elsevier Ltd. All rights reserved.

Monte Carlo Simulation of Effective Coordination Mechanisms for e-Commerce

NASA Astrophysics Data System (ADS)

Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

2008-11-01

Making decisions in a dynamic environment is considered extremely important in today's market. Decision trees which can be used to model these systems, are not easily constructed and solved, especially in the case of infinite sets of consequences (for example, consider the case where only the mean and the variance of an outcome is known). In this work, discrete approximation and Monte Carlo techniques are used to overcome the aforementioned difficulties.

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PubMed

Jiao, Y; Chen, R; Ke, X; Cheng, L; Chu, K; Lu, Z; Herskovits, E H

2011-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.

The application of a decision tree to establish the parameters associated with hypertension.

PubMed

Tayefi, Maryam; Esmaeili, Habibollah; Saberi Karimian, Maryam; Amirabadi Zadeh, Alireza; Ebrahimi, Mahmoud; Safarian, Mohammad; Nematy, Mohsen; Parizadeh, Seyed Mohammad Reza; Ferns, Gordon A; Ghayour-Mobarhan, Majid

2017-02-01

Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Identifying the performance characteristics of a winning outcome in elite mixed martial arts competition.

PubMed

James, Lachlan P; Robertson, Sam; Haff, G Gregory; Beckman, Emma M; Kelly, Vincent G

2017-03-01

To determine those performance indicators that have the greatest influence on classifying outcome at the elite level of mixed martial arts (MMA). A secondary objective was to establish the efficacy of decision tree analysis in explaining the characteristics of victory when compared to alternate statistical methods. Cross-sectional observational. Eleven raw performance indicators from male Ultimate Fighting Championship bouts (n=234) from July 2014 to December 2014 were screened for analysis. Each raw performance indicator was also converted to a rate-dependent measure to be scaled to fight duration. Further, three additional performance indicators were calculated from the dataset and included in the analysis. Cohen's d effect sizes were employed to determine the magnitude of the differences between Wins and Losses, while decision tree (chi-square automatic interaction detector (CHAID)) and discriminant function analyses (DFA) were used to classify outcome (Win and Loss). Effect size comparisons revealed differences between Wins and Losses across a number of performance indicators. Decision tree (raw: 71.8%; rate-scaled: 76.3%) and DFA (raw: 71.4%; rate-scaled 71.2%) achieved similar classification accuracies. Grappling and accuracy performance indicators were the most influential in explaining outcome. The decision tree models also revealed multiple combinations of performance indicators leading to victory. The decision tree analyses suggest that grappling activity and technique accuracy are of particular importance in achieving victory in elite-level MMA competition. The DFA results supported the importance of these performance indicators. Decision tree induction represents an intuitive and slightly more accurate approach to explaining bout outcome in this sport when compared to DFA. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Evaluation with Decision Trees of Efficacy and Safety of Semirigid Ureteroscopy in the Treatment of Proximal Ureteral Calculi.

PubMed

Sancak, Eyup Burak; Kılınç, Muhammet Fatih; Yücebaş, Sait Can

2017-01-01

The decision on the choice of proximal ureteral stone therapy depends on many factors, and sometimes urologists have difficulty in choosing the treatment option. This study is aimed at evaluating the factors affecting the success of semirigid ureterorenoscopy (URS) using the "decision tree" method. From January 2005 to November 2015, the data of consecutive patients treated for proximal ureteral stone were retrospectively analyzed. A total of 920 patients with proximal ureteral stone treated with semirigid URS were included in the study. All statistically significant attributes were tested using the decision tree method. The model created using decision tree had a sensitivity of 0.993 and an accuracy of 0.857. While URS treatment was successful in 752 patients (81.7%), it was unsuccessful in 168 patients (18.3%). According to the decision tree method, the most important factor affecting the success of URS is whether the stone is impacted to the ureteral wall. The second most important factor affecting treatment was intramural stricture requiring dilatation if the stone is impacted, and the size of the stone if not impacted. Our study suggests that the impacted stone, intramural stricture requiring dilatation and stone size may have a significant effect on the success rate of semirigid URS for proximal ureteral stone. Further studies with population-based and longitudinal design should be conducted to confirm this finding. © 2017 S. Karger AG, Basel.

C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

Planning effectiveness may grow on fault trees.

PubMed

Chow, C W; Haddad, K; Mannino, B

1991-10-01

The first step of a strategic planning process--identifying and analyzing threats and opportunities--requires subjective judgments. By using an analytical tool known as a fault tree, healthcare administrators can reduce the unreliability of subjective decision making by creating a logical structure for problem solving and decision making. A case study of 11 healthcare administrators showed that an analysis technique called prospective hindsight can add to a fault tree's ability to improve a strategic planning process.

Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil.

PubMed

Bennema, S C; Molento, M B; Scholte, R G; Carvalho, O S; Pritsch, I

2017-11-01

Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.

Model-Based Design of Tree WSNs for Decentralized Detection.

PubMed

Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam

2015-08-20

The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches.

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

NASA Technical Reports Server (NTRS)

Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

1993-01-01

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

Prescriptive models to support decision making in genetics.

PubMed

Pauker, S G; Pauker, S P

1987-01-01

Formal prescriptive models can help patients and clinicians better understand the risks and uncertainties they face and better formulate well-reasoned decisions. Using Bayes rule, the clinician can interpret pedigrees, historical data, physical findings and laboratory data, providing individualized probabilities of various diagnoses and outcomes of pregnancy. With the advent of screening programs for genetic disease, it becomes increasingly important to consider the prior probabilities of disease when interpreting an abnormal screening test result. Decision trees provide a convenient formalism for structuring diagnostic, therapeutic and reproductive decisions; such trees can also enhance communication between clinicians and patients. Utility theory provides a mechanism for patients to understand the choices they face and to communicate their attitudes about potential reproductive outcomes in a manner which encourages the integration of those attitudes into appropriate decisions. Using a decision tree, the relevant probabilities and the patients' utilities, physicians can estimate the relative worth of various medical and reproductive options by calculating the expected utility of each. By performing relevant sensitivity analyses, clinicians and patients can understand the impact of various soft data, including the patients' attitudes toward various health outcomes, on the decision making process. Formal clinical decision analytic models can provide deeper understanding and improved decision making in clinical genetics.

Using Predictive Analytics to Predict Power Outages from Severe Weather

NASA Astrophysics Data System (ADS)

Wanik, D. W.; Anagnostou, E. N.; Hartman, B.; Frediani, M. E.; Astitha, M.

2015-12-01

The distribution of reliable power is essential to businesses, public services, and our daily lives. With the growing abundance of data being collected and created by industry (i.e. outage data), government agencies (i.e. land cover), and academia (i.e. weather forecasts), we can begin to tackle problems that previously seemed too complex to solve. In this session, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. We will show a performance evaluation of outage predictive models built for Eversource Energy (formerly Connecticut Light & Power) for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to >15,000 outages). High resolution weather simulations (simulated with the Weather and Research Forecast Model) were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes.

Longleaf pine

Treesearch

William D. Boyer; Donald W. Patterson

1983-01-01

Abstract:This report describes the longleaf pine forest type and the characteristics of both tree and forest that can affect management decisions.Longleaf pine is highly adaptable to a range of management goals and silvicultural systems.Management options and appropriate silvicultural methods for the regeneration and management of this species are...

Using Induction to Refine Information Retrieval Strategies

NASA Technical Reports Server (NTRS)

Baudin, Catherine; Pell, Barney; Kedar, Smadar

1994-01-01

Conceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.

Applications of urban tree canopy assessment and prioritization tools: supporting collaborative decision making to achieve urban sustainability goals

Treesearch

Dexter H. Locke; J. Morgan Grove; Michael Galvin; Jarlath P.M. ONeil-Dunne; Charles Murphy

2013-01-01

Urban Tree Canopy (UTC) Prioritizations can be both a set of geographic analysis tools and a planning process for collaborative decision-making. In this paper, we describe how UTC Prioritizations can be used as a planning process to provide decision support to multiple government agencies, civic groups and private businesses to aid in reaching a canopy target. Linkages...

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

New Splitting Criteria for Decision Trees in Stationary Data Streams.

PubMed

Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Rutkowski, Leszek; Duda, Piotr; Jaworski, Maciej

2018-06-01

The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely heuristic procedure; for example, classical decision trees such as ID3 or CART cannot be adopted to data stream mining using Hoeffding's inequality. Therefore, there is an urgent need to develop new algorithms, which are both mathematically justified and characterized by good performance. In this paper, we address this problem by developing a family of new splitting criteria for classification in stationary data streams and investigating their probabilistic properties. The new criteria, derived using appropriate statistical tools, are based on the misclassification error and the Gini index impurity measures. The general division of splitting criteria into two types is proposed. Attributes chosen based on type- splitting criteria guarantee, with high probability, the highest expected value of split measure. Type- criteria ensure that the chosen attribute is the same, with high probability, as it would be chosen based on the whole infinite data stream. Moreover, in this paper, two hybrid splitting criteria are proposed, which are the combinations of single criteria based on the misclassification error and Gini index.

Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation.

PubMed

Tanaka, Tomohiro; Voigt, Michael D

2018-03-01

Non-melanoma skin cancer (NMSC) is the most common de novo malignancy in liver transplant (LT) recipients; it behaves more aggressively and it increases mortality. We used decision tree analysis to develop a tool to stratify and quantify risk of NMSC in LT recipients. We performed Cox regression analysis to identify which predictive variables to enter into the decision tree analysis. Data were from the Organ Procurement Transplant Network (OPTN) STAR files of September 2016 (n = 102984). NMSC developed in 4556 of the 105984 recipients, a mean of 5.6 years after transplant. The 5/10/20-year rates of NMSC were 2.9/6.3/13.5%, respectively. Cox regression identified male gender, Caucasian race, age, body mass index (BMI) at LT, and sirolimus use as key predictive or protective factors for NMSC. These factors were entered into a decision tree analysis. The final tree stratified non-Caucasians as low risk (0.8%), and Caucasian males > 47 years, BMI < 40 who did not receive sirolimus, as high risk (7.3% cumulative incidence of NMSC). The predictions in the derivation set were almost identical to those in the validation set (r 2  = 0.971, p < 0.0001). Cumulative incidence of NMSC in low, moderate and high risk groups at 5/10/20 year was 0.5/1.2/3.3, 2.1/4.8/11.7 and 5.6/11.6/23.1% (p < 0.0001). The decision tree model accurately stratifies the risk of developing NMSC in the long-term after LT.

IND - THE IND DECISION TREE PACKAGE

NASA Technical Reports Server (NTRS)

Buntine, W.

1994-01-01

A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms are CART (by Breiman Friedman, Olshen and Stone) and ID3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis. IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection. IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a CART-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite. IND consists of four basic kinds of routines: data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The generation routines are used to build classifiers. The test routines are used to evaluate classifiers and to classify data using a classifier. And the display routines are used to display classifiers in various formats. IND is written in C-language for Sun4 series computers. It consists of several programs with controlling shell scripts. Extensive UNIX man entries are included. IND is designed to be used on any UNIX system, although it has only been thoroughly tested on SUN platforms. The standard distribution medium for IND is a .25 inch streaming magnetic tape cartridge in UNIX tar format. An electronic copy of the documentation in PostScript format is included on the distribution medium. IND was developed in 1992.

Interpretation of diagnostic data: 6. How to do it with more complex maths.

PubMed

1983-11-15

We have now shown you how to use decision analysis in making those rare, tough diagnostic decisions that are not soluble through other, easier routes. In summary, to "use more complex maths" the following steps will be useful: Create a decision tree or map of all the pertinent courses of action and their consequences. Assign probabilities to the branches of each chance node. Assign utilities to each of the potential outcomes shown on the decision tree. Combine the probabilities and utilities for each node on the decision tree. Pick the decision that leads to the highest expected utility. Test your decision for its sensitivity to clinically sensible changes in probabilities and utilities. That concludes this series of clinical epidemiology rounds. You've come a long way from "doing it with pictures" and are now able to extract most of the diagnostic information that can be provided from signs, symptoms and laboratory investigations. We would appreciate learning whether you have found this series useful and how we can do a better job of presenting these and other elements of "the science of the art of medicine".

Policy Route Map for Academic Libraries' Digital Content

ERIC Educational Resources Information Center

Koulouris, Alexandros; Kapidakis, Sarantos

2012-01-01

This paper presents a policy decision tree for digital information management in academic libraries. The decision tree is a policy guide, which offers alternative access and reproduction policy solutions according to the prevailing circumstances (for example acquisition method, copyright ownership). It refers to the digital information life cycle,…

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Naturalistic Decision Making for Power System Operators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greitzer, Frank L.; Podmore, Robin; Robinson, Marck

2010-02-01

Motivation – Investigations of large-scale outages in the North American interconnected electric system often attribute the causes to three T’s: Trees, Training and Tools. To document and understand the mental processes used by expert operators when making critical decisions, a naturalistic decision making (NDM) model was developed. Transcripts of conversations were analyzed to reveal and assess NDM-based performance criteria. Findings/Design – An item analysis indicated that the operators’ Situation Awareness Levels, mental models, and mental simulations can be mapped at different points in the training scenario. This may identify improved training methods or analytical/ visualization tools. Originality/Value – This studymore » applies for the first time, the concepts of Recognition Primed Decision Making, Situation Awareness Levels and Cognitive Task Analysis to training of electric power system operators. Take away message – The NDM approach provides a viable framework for systematic training management to accelerate learning in simulator-based training scenarios for power system operators and teams.« less

Individualized Prediction of Heat Stress in Firefighters: A Data-Driven Approach Using Classification and Regression Trees.

PubMed

Mani, Ashutosh; Rao, Marepalli; James, Kelley; Bhattacharya, Amit

2015-01-01

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-30

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-01

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Central States forest management guides as applied in STEMS.

Treesearch

Nancy R. Walters

1988-01-01

Describes a management prescription system for Central States cover types developed for use in the Central States Stand and Tree Evaluation and Modeling System (STEMS). It includes one management guide for each of the six major cover types in the region. Each guide consists of a decision key that prescribes management, based on stand characteristics and a set of...

Partial dependence of breast tumor malignancy on ultrasound image features derived from boosted trees

NASA Astrophysics Data System (ADS)

Yang, Wei; Zhang, Su; Li, Wenying; Chen, Yaqing; Lu, Hongtao; Chen, Wufan; Chen, Yazhu

2010-04-01

Various computerized features extracted from breast ultrasound images are useful in assessing the malignancy of breast tumors. However, the underlying relationship between the computerized features and tumor malignancy may not be linear in nature. We use the decision tree ensemble trained by the cost-sensitive boosting algorithm to approximate the target function for malignancy assessment and to reflect this relationship qualitatively. Partial dependence plots are employed to explore and visualize the effect of features on the output of the decision tree ensemble. In the experiments, 31 image features are extracted to quantify the sonographic characteristics of breast tumors. Patient age is used as an external feature because of its high clinical importance. The area under the receiver-operating characteristic curve of the tree ensembles can reach 0.95 with sensitivity of 0.95 (61/64) at the associated specificity 0.74 (77/104). The partial dependence plots of the four most important features are demonstrated to show the influence of the features on malignancy, and they are in accord with the empirical observations. The results can provide visual and qualitative references on the computerized image features for physicians, and can be useful for enhancing the interpretability of computer-aided diagnosis systems for breast ultrasound.

Efficient cost-sensitive human-machine collaboration for offline signature verification

NASA Astrophysics Data System (ADS)

Coetzer, Johannes; Swanepoel, Jacques; Sabourin, Robert

2012-01-01

We propose a novel strategy for the optimal combination of human and machine decisions in a cost-sensitive environment. The proposed algorithm should be especially beneficial to financial institutions where off-line signatures, each associated with a specific transaction value, require authentication. When presented with a collection of genuine and fraudulent training signatures, produced by so-called guinea pig writers, the proficiency of a workforce of human employees and a score-generating machine can be estimated and represented in receiver operating characteristic (ROC) space. Using a set of Boolean fusion functions, the majority vote decision of the human workforce is combined with each threshold-specific machine-generated decision. The performance of the candidate ensembles is estimated and represented in ROC space, after which only the optimal ensembles and associated decision trees are retained. When presented with a questioned signature linked to an arbitrary writer, the system first uses the ROC-based cost gradient associated with the transaction value to select the ensemble that minimises the expected cost, and then uses the corresponding decision tree to authenticate the signature in question. We show that, when utilising the entire human workforce, the incorporation of a machine streamlines the authentication process and decreases the expected cost for all operating conditions.

CorRECTreatment: A Web-based Decision Support Tool for Rectal Cancer Treatment that Uses the Analytic Hierarchy Process and Decision Tree

PubMed Central

Karakülah, G.; Dicle, O.; Sökmen, S.; Çelikoğlu, C.C.

2015-01-01

Summary Background The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making. Objective The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. Methods The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. Results In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. Conclusions The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options. PMID:25848413

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree.

PubMed

Suner, A; Karakülah, G; Dicle, O; Sökmen, S; Çelikoğlu, C C

2015-01-01

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making. The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

Spatial distribution of block falls using volumetric GIS-decision-tree models

NASA Astrophysics Data System (ADS)

Abdallah, C.

2010-10-01

Block falls are considered a significant aspect of surficial instability contributing to losses in land and socio-economic aspects through their damaging effects to natural and human environments. This paper predicts and maps the geographic distribution and volumes of block falls in central Lebanon using remote sensing, geographic information systems (GIS) and decision-tree modeling (un-pruned and pruned trees). Eleven terrain parameters (lithology, proximity to fault line, karst type, soil type, distance to drainage line, elevation, slope gradient, slope aspect, slope curvature, land cover/use, and proximity to roads) were generated to statistically explain the occurrence of block falls. The latter were discriminated using SPOT4 satellite imageries, and their dimensions were determined during field surveys. The un-pruned tree model based on all considered parameters explained 86% of the variability in field block fall measurements. Once pruned, it classifies 50% in block falls' volumes by selecting just four parameters (lithology, slope gradient, soil type, and land cover/use). Both tree models (un-pruned and pruned) were converted to quantitative 1:50,000 block falls' maps with different classes; starting from Nil (no block falls) to more than 4000 m 3. These maps are fairly matching with coincidence value equal to 45%; however, both can be used to prioritize the choice of specific zones for further measurement and modeling, as well as for land-use management. The proposed tree models are relatively simple, and may also be applied to other areas (i.e. the choice of un-pruned or pruned model is related to the availability of terrain parameters in a given area).

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

Model-Based Design of Tree WSNs for Decentralized Detection †

PubMed Central

Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam

2015-01-01

The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches. PMID:26307989

What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

2004-01-01

To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.

Knowledge Guided Evolutionary Algorithms in Financial Investing

ERIC Educational Resources Information Center

Wimmer, Hayden

2013-01-01

A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions--or traversing the search space--at random exhibits no control…

A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/CT.

PubMed

Pak, Kyoungjune; Kim, Keunyoung; Kim, Mi-Hyun; Eom, Jung Seop; Lee, Min Ki; Cho, Jeong Su; Kim, Yun Seong; Kim, Bum Soo; Kim, Seong Jang; Kim, In Joo

2018-01-01

We aimed to develop a decision tree model to improve diagnostic performance of positron emission tomography/computed tomography (PET/CT) to detect metastatic lymph nodes (LN) in non-small cell lung cancer (NSCLC). 115 patients with NSCLC were included in this study. The training dataset included 66 patients. A decision tree model was developed with 9 variables, and validated with 49 patients: short and long diameters of LNs, ratio of short and long diameters, maximum standardized uptake value (SUVmax) of LN, mean hounsfield unit, ratio of LN SUVmax and ascending aorta SUVmax (LN/AA), and ratio of LN SUVmax and superior vena cava SUVmax. A total of 301 LNs of 115 patients were evaluated in this study. Nodular calcification was applied as the initial imaging parameter, and LN SUVmax (≥3.95) was assessed as the second. LN/AA (≥2.92) was required to high LN SUVmax. Sensitivity was 50% for training dataset, and 40% for validation dataset. However, specificity was 99.28% for training dataset, and 96.23% for validation dataset. In conclusion, we have developed a new decision tree model for interpreting mediastinal LNs. All LNs with nodular calcification were benign, and LNs with high LN SUVmax and high LN/AA were metastatic Further studies are needed to incorporate subjective parameters and pathologic evaluations into a decision tree model to improve the test performance of PET/CT.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

PubMed

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

Fish to meat intake ratio and cooking oils are associated with hepatitis C virus carriers with persistently normal alanine aminotransferase levels.

PubMed

Otsuka, Momoka; Uchida, Yuki; Kawaguchi, Takumi; Taniguchi, Eitaro; Kawaguchi, Atsushi; Kitani, Shingo; Itou, Minoru; Oriishi, Tetsuharu; Kakuma, Tatsuyuki; Tanaka, Suiko; Yagi, Minoru; Sata, Michio

2012-10-01

  Dietary habits are involved in the development of chronic inflammation; however, the impact of dietary profiles of hepatitis C virus carriers with persistently normal alanine transaminase levels (HCV-PNALT) remains unclear. The decision-tree algorithm is a data-mining statistical technique, which uncovers meaningful profiles of factors from a data collection. We aimed to investigate dietary profiles associated with HCV-PNALT using a decision-tree algorithm.   Twenty-seven HCV-PNALT and 41 patients with chronic hepatitis C were enrolled in this study. Dietary habit was assessed using a validated semiquantitative food frequency questionnaire. A decision-tree algorithm was created by dietary variables, and was evaluated by area under the receiver operating characteristic curve analysis (AUROC).   In multivariate analysis, fish to meat ratio, dairy product and cooking oils were identified as independent variables associated with HCV-PNALT. The decision-tree algorithm was created with two variables: a fish to meat ratio and cooking oils/ideal bodyweight. When subjects showed a fish to meat ratio of 1.24 or more, 68.8% of the subjects were HCV-PNALT. On the other hand, 11.5% of the subjects were HCV-PNALT when subjects showed a fish to meat ratio of less than 1.24 and cooking oil/ideal bodyweight of less than 0.23 g/kg. The difference in the proportion of HCV-PNALT between these groups are significant (odds ratio 16.87, 95% CI 3.40-83.67, P = 0.0005). Fivefold cross-validation of the decision-tree algorithm showed an AUROC of 0.6947 (95% CI 0.5656-0.8238, P = 0.0067).   The decision-tree algorithm disclosed that fish to meat ratio and cooking oil/ideal bodyweight were associated with HCV-PNALT. © 2012 The Japan Society of Hepatology.

Prioritization of information using decision support systems for seismic risk in Bucharest city

NASA Astrophysics Data System (ADS)

Armas, Iuliana; Gheorghe, Diana

2014-05-01

Nowadays, because of the ever increasing volume of information, policymakers are faced with decision making problems. Achieving an objective and suitable decision making may become a challenge. In such situations decision support systems (DSS) have been developed. DSS can assist in the decision making process, offering support on how a decision should be made, rather than what decision should be made (Simon, 1979). This in turn potentially involves a huge number of stakeholders and criteria. Regarding seismic risk, Bucharest City is highly vulnerable (Mandrescu et al., 2007). The aim of this study is to implement a spatial decision support system in order to secure a suitable shelter in case of an earthquake occurrence in the historical centre of Bucharest City. In case of a seismic risk, a shelter is essential for sheltering people who lost their homes or whose homes are in danger of collapsing while people at risk receive first aid in the post-disaster phase. For the present study, the SMCE Module for ILWIS 3.4 was used. The methodology included structuring the problem by creating a decision tree, standardizing and weighting of the criteria. The results showed that the most suitable buildings are Tania Hotel, Hanul lui Manuc, The National Bank of Romania, The Romanian Commercial Bank and The National History Museum.

Validating a decision tree for serious infection: diagnostic accuracy in acutely ill children in ambulatory care.

PubMed

Verbakel, Jan Y; Lemiengre, Marieke B; De Burghgraeve, Tine; De Sutter, An; Aertgeerts, Bert; Bullens, Dominique M A; Shinkins, Bethany; Van den Bruel, Ann; Buntinx, Frank

2015-08-07

Acute infection is the most common presentation of children in primary care with only few having a serious infection (eg, sepsis, meningitis, pneumonia). To avoid complications or death, early recognition and adequate referral are essential. Clinical prediction rules have the potential to improve diagnostic decision-making for rare but serious conditions. In this study, we aimed to validate a recently developed decision tree in a new but similar population. Diagnostic accuracy study validating a clinical prediction rule. Acutely ill children presenting to ambulatory care in Flanders, Belgium, consisting of general practice and paediatric assessment in outpatient clinics or the emergency department. Physicians were asked to score the decision tree in every child. The outcome of interest was hospital admission for at least 24 h with a serious infection within 5 days after initial presentation. We report the diagnostic accuracy of the decision tree in sensitivity, specificity, likelihood ratios and predictive values. In total, 8962 acute illness episodes were included, of which 283 lead to admission to hospital with a serious infection. Sensitivity of the decision tree was 100% (95% CI 71.5% to 100%) at a specificity of 83.6% (95% CI 82.3% to 84.9%) in the general practitioner setting with 17% of children testing positive. In the paediatric outpatient and emergency department setting, sensitivities were below 92%, with specificities below 44.8%. In an independent validation cohort, this clinical prediction rule has shown to be extremely sensitive to identify children at risk of hospital admission for a serious infection in general practice, making it suitable for ruling out. NCT02024282. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Decay fungi of oaks and associated hardwoods for western arborists

Treesearch

Jessie A. Glaeser; Kevin T. Smith

2010-01-01

Examination of trees for the presence and extent of decay should be part of any hazard tree assessment. Identification of the fungi responsible for the decay improves prediction of tree performance and the quality of management decisions, including tree pruning or removal. Scouting for Sudden Oak Death (SOD) in the West has drawn attention to hardwood tree species,...

A Swarm Optimization approach for clinical knowledge mining.

PubMed

Christopher, J Jabez; Nehemiah, H Khanna; Kannan, A

2015-10-01

Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty.

PubMed

Cheaib, Alissar; Badeau, Vincent; Boe, Julien; Chuine, Isabelle; Delire, Christine; Dufrêne, Eric; François, Christophe; Gritti, Emmanuel S; Legay, Myriam; Pagé, Christian; Thuiller, Wilfried; Viovy, Nicolas; Leadley, Paul

2012-06-01

Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion. © 2012 Blackwell Publishing Ltd/CNRS.

A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

Treesearch

Rey S. Ofren; Edward Harvey

2000-01-01

A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

Which Types of Leadership Styles Do Followers Prefer? A Decision Tree Approach

ERIC Educational Resources Information Center

Salehzadeh, Reza

2017-01-01

Purpose: The purpose of this paper is to propose a new method to find the appropriate leadership styles based on the followers' preferences using the decision tree technique. Design/methodology/approach: Statistical population includes the students of the University of Isfahan. In total, 750 questionnaires were distributed; out of which, 680…

The Americans with Disabilities Act: A Decision Tree for Social Services Administrators

ERIC Educational Resources Information Center

O'Brien, Gerald V.; Ellegood, Christina

2005-01-01

The 1990 Americans with Disabilities Act has had a profound influence on social workers and social services administrators in virtually all work settings. Because of the multiple elements of the act, however, assessing the validity of claims can be a somewhat arduous and complicated task. This article provides a "decision tree" for…

A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chu, Hui-Chun; Shih, Ju-Ling; Huang, Shu-Hsien; Tsai, Chin-Chung

2010-01-01

A context-aware ubiquitous learning environment is an authentic learning environment with personalized digital supports. While showing the potential of applying such a learning environment, researchers have also indicated the challenges of providing adaptive and dynamic support to individual students. In this paper, a decision-tree-oriented…

A decision tree approach using silvics to guide planning for forest restoration

Treesearch

Sharon M. Hermann; John S. Kush; John C. Gilbert

2013-01-01

We created a decision tree based on silvics of longleaf pine (Pinus palustris) and historical descriptions to develop approaches for restoration management at Horseshoe Bend National Military Park located in central Alabama. A National Park Service goal is to promote structure and composition of a forest that likely surrounded the 1814 battlefield....

What Satisfies Students? Mining Student-Opinion Data with Regression and Decision-Tree Analysis. AIR 2002 Forum Paper.

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…

Assessment of the potential enhancement of rural food security in Mexico using decision tree land use classification on medium resolution satellite imagery

NASA Astrophysics Data System (ADS)

Bermeo, A.; Couturier, S.

2017-01-01

Because of its renewed importance in international agendas, food security in sub-tropical countries has been the object of studies at different scales, although the spatial components of food security are still largely undocumented. Among other aspects, food security can be assessed using a food selfsufficiency index. We propose a spatial representation of this assessment in the densely populated rural area of the Huasteca Poblana, Mexico, where there is a known tendency towards the loss of selfsufficiency of basic grains. The main agricultural systems in this area are the traditional milpa (a multicrop practice with maize as the main basic crop) system, coffee plantations and grazing land for bovine livestock. We estimate a potential additional milpa - based maize production by smallholders identifying the presence of extensive coffee and pasture systems in the production data of the agricultural census. The surface of extensive coffee plantations and pasture land were estimated using the detailed coffee agricultural census data, and a decision tree combining unsupervised and supervised spectral classification techniques of medium scale (Landsat) satellite imagery. We find that 30% of the territory would benefit more than 50% increment in food security and 13% could theoretically become maize self-sufficient from the conversion of extensive systems to the traditional multicrop milpa system.

Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality

PubMed Central

Vergara, Pablo M.; Soto, Gerardo E.; Rodewald, Amanda D.; Meneses, Luis O.; Pérez-Hernández, Christian G.

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox’s proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales. PMID:27416115

Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality.

PubMed

Vergara, Pablo M; Soto, Gerardo E; Moreira-Arce, Darío; Rodewald, Amanda D; Meneses, Luis O; Pérez-Hernández, Christian G

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox's proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales.

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kupriyanov, M. S., E-mail: mikhail.kupriyanov@gmail.com; Shukeilo, E. Y., E-mail: eyshukeylo@gmail.com; Shichkina, J. A., E-mail: strange.y@mail.ru

2015-11-17

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient’s health condition using data from a wearable device considers in this article.

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

NASA Astrophysics Data System (ADS)

Kupriyanov, M. S.; Shukeilo, E. Y.; Shichkina, J. A.

2015-11-01

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient's health condition using data from a wearable device considers in this article.

Statistical analysis of texture in trunk images for biometric identification of tree species.

PubMed

Bressane, Adriano; Roveda, José A F; Martins, Antônio C G

2015-04-01

The identification of tree species is a key step for sustainable management plans of forest resources, as well as for several other applications that are based on such surveys. However, the present available techniques are dependent on the presence of tree structures, such as flowers, fruits, and leaves, limiting the identification process to certain periods of the year. Therefore, this article introduces a study on the application of statistical parameters for texture classification of tree trunk images. For that, 540 samples from five Brazilian native deciduous species were acquired and measures of entropy, uniformity, smoothness, asymmetry (third moment), mean, and standard deviation were obtained from the presented textures. Using a decision tree, a biometric species identification system was constructed and resulted to a 0.84 average precision rate for species classification with 0.83accuracy and 0.79 agreement. Thus, it can be considered that the use of texture presented in trunk images can represent an important advance in tree identification, since the limitations of the current techniques can be overcome.

Protein attributes contribute to halo-stability, bioinformatics approach

PubMed Central

2011-01-01

Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins. PMID:21592393

Classification tree for the assessment of sedentary lifestyle among hypertensive.

PubMed

Castelo Guedes Martins, Larissa; Venícios de Oliveira Lopes, Marcos; Gomes Guedes, Nirla; Paixão de Menezes, Angélica; de Oliveira Farias, Odaleia; Alves Dos Santos, Naftale

2016-04-01

To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

Enhancement of Fast Face Detection Algorithm Based on a Cascade of Decision Trees

NASA Astrophysics Data System (ADS)

Khryashchev, V. V.; Lebedev, A. A.; Priorov, A. L.

2017-05-01

Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. Final realization of given algorithm consist of 5 different cascades for frontal/non-frontal faces. One more thing which we take from the simulation results is a low computational complexity of CEDT algorithm in comparison with standard Viola-Jones approach. This could prove important in the embedded system and mobile device industries because it can reduce the cost of hardware and make battery life longer.

A key for the Forest Service hardwood tree grades

Treesearch

Gary W. Miller; Leland F. Hanks; Harry V., Jr. Wiant

1986-01-01

A dichotomous key organizes the USDA Forest Service hardwood tree grade specifications into a stepwise procedure for those learning to grade hardwood sawtimber. The key addresses the major grade factors, tree size, surface characteristics, and allowable cull deductions in a series of paried choices that lead the user to a decision regarding tree grade.

Inferences from growing trees backwards

Treesearch

David W. Green; Kent A. McDonald

1997-01-01

The objective of this paper is to illustrate how longitudinal stress wave techniques can be useful in tracking the future quality of a growing tree. Monitoring the quality of selected trees in a plantation forest could provide early input to decisions on the effectiveness of management practices, or future utilization options, for trees in a plantation. There will...

Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.

PubMed

Morales, Susana; Barros, Jorge; Echávarri, Orietta; García, Fabián; Osses, Alex; Moya, Claudia; Maino, María Paz; Fischman, Ronit; Núñez, Catalina; Szmulewicz, Tita; Tomicic, Alemka

2017-01-01

In efforts to develop reliable methods to detect the likelihood of impending suicidal behaviors, we have proposed the following. To gain a deeper understanding of the state of suicide risk by determining the combination of variables that distinguishes between groups with and without suicide risk. A study involving 707 patients consulting for mental health issues in three health centers in Greater Santiago, Chile. Using 345 variables, an analysis was carried out with artificial intelligence tools, Cross Industry Standard Process for Data Mining processes, and decision tree techniques. The basic algorithm was top-down, and the most suitable division produced by the tree was selected by using the lowest Gini index as a criterion and by looping it until the condition of belonging to the group with suicidal behavior was fulfilled. Four trees distinguishing the groups were obtained, of which the elements of one were analyzed in greater detail, since this tree included both clinical and personality variables. This specific tree consists of six nodes without suicide risk and eight nodes with suicide risk (tree decision 01, accuracy 0.674, precision 0.652, recall 0.678, specificity 0.670, F measure 0.665, receiver operating characteristic (ROC) area under the curve (AUC) 73.35%; tree decision 02, accuracy 0.669, precision 0.642, recall 0.694, specificity 0.647, F measure 0.667, ROC AUC 68.91%; tree decision 03, accuracy 0.681, precision 0.675, recall 0.638, specificity 0.721, F measure, 0.656, ROC AUC 65.86%; tree decision 04, accuracy 0.714, precision 0.734, recall 0.628, specificity 0.792, F measure 0.677, ROC AUC 58.85%). This study defines the interactions among a group of variables associated with suicidal ideation and behavior. By using these variables, it may be possible to create a quick and easy-to-use tool. As such, psychotherapeutic interventions could be designed to mitigate the impact of these variables on the emotional state of individuals, thereby reducing eventual risk of suicide. Such interventions may reinforce psychological well-being, feelings of self-worth, and reasons for living, for each individual in certain groups of patients.

Toward the Decision Tree for Inferring Requirements Maturation Types

NASA Astrophysics Data System (ADS)

Nakatani, Takako; Kondo, Narihito; Shirogane, Junko; Kaiya, Haruhiko; Hori, Shozo; Katamine, Keiichi

Requirements are elicited step by step during the requirements engineering (RE) process. However, some types of requirements are elicited completely after the scheduled requirements elicitation process is finished. Such a situation is regarded as problematic situation. In our study, the difficulties of eliciting various kinds of requirements is observed by components. We refer to the components as observation targets (OTs) and introduce the word “Requirements maturation.” It means when and how requirements are elicited completely in the project. The requirements maturation is discussed on physical and logical OTs. OTs Viewed from a logical viewpoint are called logical OTs, e.g. quality requirements. The requirements of physical OTs, e.g., modules, components, subsystems, etc., includes functional and non-functional requirements. They are influenced by their requesters' environmental changes, as well as developers' technical changes. In order to infer the requirements maturation period of each OT, we need to know how much these factors influence the OTs' requirements maturation. According to the observation of actual past projects, we defined the PRINCE (Pre Requirements Intelligence Net Consideration and Evaluation) model. It aims to guide developers in their observation of the requirements maturation of OTs. We quantitatively analyzed the actual cases with their requirements elicitation process and extracted essential factors that influence the requirements maturation. The results of interviews of project managers are analyzed by WEKA, a data mining system, from which the decision tree was derived. This paper introduces the PRINCE model and the category of logical OTs to be observed. The decision tree that helps developers infer the maturation type of an OT is also described. We evaluate the tree through real projects and discuss its ability to infer the requirements maturation types.

Uninjured trees - a meaningful guide to white-pine weevil control decisions

Treesearch

William E. Waters

1962-01-01

The white-pine weevil, Pissodes strobi, is a particularly insidious forest pest that can render a stand of host trees virtually worthless. It rarely, if ever, kills a tree; but the crooks, forks, and internal defects that develop in attacked trees over a period of years may reduce the merchantable volume and value of the tree at harvest age to zero. Dollar losses are...

Compensatory value of urban trees in the United States

Treesearch

David J. Nowak; Daniel E. Crane; John F. Dwyer

2002-01-01

Understanding the value of an urban forest can give decision makers a better foundation for urban tree namagement. Based on tree-valuation methods of the Council of Tree and Landscape Appraisers and field data from eight cities, total compensatory value of tree populations in U.S. cities ranges from $101 million in Jersey City, New Jersey, to $6.2 billion in New York,...

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

PubMed

Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

2018-01-01

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Data mining of space heating system performance in affordable housing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Xiaoxin; Yan, Da; Hong, Tianzhen

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems inmore » terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. In conclusion, from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.« less

Data mining of space heating system performance in affordable housing

DOE PAGES

Ren, Xiaoxin; Yan, Da; Hong, Tianzhen

2015-02-16

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems inmore » terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. In conclusion, from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.« less

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Prediction of adverse drug reactions using decision tree modeling.

PubMed

Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J

2010-07-01

Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

Prognostic Factors and Decision Tree for Long-term Survival in Metastatic Uveal Melanoma.

PubMed

Lorenzo, Daniel; Ochoa, María; Piulats, Josep Maria; Gutiérrez, Cristina; Arias, Luis; Català, Jaum; Grau, María; Peñafiel, Judith; Cobos, Estefanía; Garcia-Bru, Pere; Rubio, Marcos Javier; Padrón-Pérez, Noel; Dias, Bruno; Pera, Joan; Caminal, Josep Maria

2017-12-04

The purpose of this study was to demonstrate the existence of a bimodal survival pattern in metastatic uveal melanoma. Secondary aims were to identify the characteristics and prognostic factors associated with long-term survival and to develop a clinical decision tree. The medical records of 99 metastatic uveal melanoma patients were retrospectively reviewed. Patients were classified as either short (≤ 12 months) or long-term survivors (> 12 months) based on a graphical interpretation of the survival curve after diagnosis of the first metastatic lesion. Ophthalmic and oncological characteristics were assessed in both groups. Of the 99 patients, 62 (62.6%) were classified as short-term survivors, and 37 (37.4%) as long-term survivors. The multivariate analysis identified the following predictors of long-term survival: age ≤ 65 years (p=0.012) and unaltered serum lactate dehydrogenase levels (p=0.018); additionally, the size (smaller vs. larger) of the largest liver metastasis showed a trend towards significance (p=0.063). Based on the variables significantly associated with long-term survival, we developed a decision tree to facilitate clinical decision-making. The findings of this study demonstrate the existence of a bimodal survival pattern in patients with metastatic uveal melanoma. The presence of certain clinical characteristics at diagnosis of distant disease is associated with long-term survival. A decision tree was developed to facilitate clinical decision-making and to counsel patients about the expected course of disease.

Energy requirements in pressure irrigation systems

NASA Astrophysics Data System (ADS)

Sánchez, R.; Rodríguez-Sinobas, L.; Juana, L.; Laguna, F. V.; Castañón, G.; Gil, M.; Benítez, J.

2012-04-01

Modernization of irrigation schemes, generally understood as transformation of surface irrigation systems into pressure -sprinkler and trickle- irrigation systems, aims at, among others, improving irrigation efficiency and reduction of operation and maintenance efforts made by the irrigators. However, pressure irrigation systems, in contrast, carry a serious energy cost. Energy requirements depend on decisions taken on management strategies during the operation phase, which are conditioned by previous decisions taken on the design project of the different elements which compose the irrigation system. Most of the countries where irrigation activity is significant bear in mind that modernization irrigation must play a key role in the agricultural infrastructure policies. The objective of this study is to characterize and estimate the mean and variation of the energy consumed by common types of irrigation systems and their management possibilities. The work includes all processes involved from the diversion of water into irrigation specific infrastructure to water discharge by the emitters installed on the crop fields. Simulation taking into account all elements comprising the irrigation system has been used to estimate the energy requirements of typical irrigation systems of several crop production systems. It has been applied to extensive and intensive crop systems, such us extensive winter crops, summer crops and olive trees, fruit trees and vineyards and intensive horticulture in greenhouses. The simulation of various types of irrigation systems and management strategies, in the framework imposed by particular cropping systems, would help to develop criteria for improving the energy balance in relation to the irrigation water supply productivity.

Test Reviews: Euler, B. L. (2007). "Emotional Disturbance Decision Tree". Lutz, FL: Psychological Assessment Resources

ERIC Educational Resources Information Center

Tansy, Michael

2009-01-01

The Emotional Disturbance Decision Tree (EDDT) is a teacher-completed norm-referenced rating scale published by Psychological Assessment Resources, Inc., in Lutz, Florida. The 156-item EDDT was developed for use as part of a broader assessment process to screen and assist in the identification of 5- to 18-year-old children for the special…

Phytotechnology Technical and Regulatory Guidance Document

DTIC Science & Technology

2001-04-01

contaminated media is rather new. Throughout the development process of this document, we referred to the science as “ phytoremediation .” Recently...the media containing contaminants, we now refer to “phytotechnologies” as the overarching terminology, while using “ phytoremediation ” more...publication of the ITRC document, Phytoremediation Decision Tree. The decision tree was designed to allow potential users to take basic information

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

PubMed

Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Khalili, Davood; Azizi, Fereidoun; Hadaegh, Farzad

2014-09-01

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

PubMed

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p < 0.05). Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

Decision tree and PCA-based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

Machine Learning Through Signature Trees. Applications to Human Speech.

ERIC Educational Resources Information Center

White, George M.

A signature tree is a binary decision tree used to classify unknown patterns. An attempt was made to develop a computer program for manipulating signature trees as a general research tool for exploring machine learning and pattern recognition. The program was applied to the problem of speech recognition to test its effectiveness for a specific…

Modeling individual tree survial

Treesearch

Quang V. Cao

2016-01-01

Information provided by growth and yield models is the basis for forest managers to make decisions on how to manage their forests. Among different types of growth models, whole-stand models offer predictions at stand level, whereas individual-tree models give detailed information at tree level. The well-known logistic regression is commonly used to predict tree...

Predicting Tillage Patterns in the Tiffin River Watershed Using Remote Sensing Methods

NASA Astrophysics Data System (ADS)

Brooks, C.; McCarty, J. L.; Dean, D. B.; Mann, B. F.

2012-12-01

Previous research in tillage mapping has focused primarily on utilizing low to no-cost, moderate (30 m to 15 m) resolution satellite data. Successful data processing techniques published in the scientific literature have focused on extracting and/or classifying tillage patterns through manipulation of spectral bands. For instance, Daughtry et al. (2005) evaluated several spectral indices for crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. A weak to moderate relationship between Landsat Thematic Mapper (TM) indices and crop residue cover was found; similar results were reported in Minnesota. Building on the findings from the scientific literature and previous work done by MTRI in the heavily agricultural Tiffin watershed of northwest Ohio and southeast Michigan, a decision tree classifier approach (also referred to as a classification tree) was used, linking several satellite data to on-the-ground tillage information in order to boost classification results. This approach included five tillage indices and derived products. A decision tree methodology enabled the development of statistically optimized (i.e., minimizing misclassification rates) classification algorithms at various desired time steps: monthly, seasonally, and annual over the 2006-2010 time period. Due to their flexibility, processing speed, and availability within all major remote sensing and statistical software packages, decision trees can ingest several data inputs from multiple sensors and satellite products, selecting only the bands, band ratios, indices, and products that further reduce misclassification errors. The project team created crop-specific tillage pattern classification trees whereby a training data set (~ 50% of available ground data) was created for production of the actual decision tree and a validation data set was set aside (~ 50% of available ground data) in order to assess the accuracy of the classification. A seasonal time step was used, optimizing a decision tree based on seasonal ground data for tillage patterns and satellite data and products for years 2006 through 2010. Annual crop type maps derived by the project team and the USDA Cropland Data Layer project was used an input to understand locations of corn, soybeans, wheat, etc. on a yearly basis. As previously stated, the robustness of the decision tree approach is the ability to implement various satellite data and products across temporal, spectral, and spatial resolutions, thereby improving the resulting classification and providing a reliable method that is not sensor-dependent. Tillage pattern classification from satellite imagery is not a simple task and has proven a challenge to previous researchers investigating this remote sensing topic. The team's decision tree method produced a practical, usable output within a focused project time period. Daughtry, C.S.T., Hunt Jr., E.R., Doraiswamy, P.C., McMurtrey III, J.E. 2005. Remote sensing the spatial distribution of crop residues. Agron. J. 97, 864-871.

Using decision tree models to depict primary care physicians CRC screening decision heuristics.

PubMed

Wackerbarth, Sarah B; Tarasenko, Yelena N; Curtis, Laurel A; Joyce, Jennifer M; Haist, Steven A

2007-10-01

The purpose of this study was to identify decision heuristics utilized by primary care physicians in formulating colorectal cancer screening recommendations. Qualitative research using in-depth semi-structured interviews. We interviewed 66 primary care internists and family physicians evenly drawn from academic and community practices. A majority of physicians were male, and almost all were white, non-Hispanic. Three researchers independently reviewed each transcript to determine the physician's decision criteria and developed decision trees. Final trees were developed by consensus. The constant comparative methodology was used to define the categories. Physicians were found to use 1 of 4 heuristics ("age 50," "age 50, if family history, then earlier," "age 50, if family history, then screen at age 40," or "age 50, if family history, then adjust relative to reference case") for the timing recommendation and 5 heuristics ["fecal occult blood test" (FOBT), "colonoscopy," "if not colonoscopy, then...," "FOBT and another test," and "a choice between options"] for the type decision. No connection was found between timing and screening type heuristics. We found evidence of heuristic use. Further research is needed to determine the potential impact on quality of care.

VHF Air/Ground Communications for Air Traffic Control: A Decision Tree Approach to System Innovations. Volume 2

DTIC Science & Technology

1993-07-01

GROUND RADIO ISSUES Multiple transmitters and receivers at a common site has been a dfficult integration issue for many communicaton systems...lower data rate vocoders become available. 10. Compatibility of modulation with channel availability and message integrity improvements, including...overrides can use in- frequency and path diversity, integrity improvements band signaling, busy channel overrides, and Frequency diversity and ARQ ARQ is

A decision tree model to estimate the value of information provided by a groundwater quality monitoring network

NASA Astrophysics Data System (ADS)

Khader, A. I.; Rosenberg, D. E.; McKee, M.

2013-05-01

Groundwater contaminated with nitrate poses a serious health risk to infants when this contaminated water is used for culinary purposes. To avoid this health risk, people need to know whether their culinary water is contaminated or not. Therefore, there is a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management options. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision-maker and the expected outcomes from these alternatives. The alternatives include (i) ignore the health risk of nitrate-contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, contaminant transport processes, and climate (Khader, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine, where methemoglobinemia (blue baby syndrome) is the main health problem associated with the principal contaminant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs include healthcare for methemoglobinemia, purchase of bottled water, and installation and maintenance of the groundwater monitoring system. At current methemoglobinemia and bottled water costs of 150/person and 0.6/baby/day, the decision tree results show that the expected cost of establishing the proposed groundwater quality monitoring network exceeds the expected costs of the uninformed alternatives and there is no value to the information the monitoring system provides. However, the monitoring system will be preferred to ignoring the health risk or using alternative sources if the methemoglobinemia cost rises to 300/person or the bottled water cost increases to 2.3/baby/day. Similarly, the monitoring system has value if the system can more accurately report actual aquifer concentrations and the public more fully abides by manager recommendations to use/not use the aquifer. The system also has value if it will serve a larger population or if its installation costs can be reduced, for example using a smaller number of monitoring wells. The VOI analysis shows how monitoring system design, accuracy, installation and operating costs, public awareness of health risks, costs of alternatives, and demographics together affect the value of implementing a system to monitor groundwater quality.

A decision tree model to estimate the value of information provided by a groundwater quality monitoring network

NASA Astrophysics Data System (ADS)

Khader, A.; Rosenberg, D.; McKee, M.

2012-12-01

Nitrate pollution poses a health risk for infants whose freshwater drinking source is groundwater. This risk creates a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision maker and the expected outcomes from these alternatives. The alternatives include: (i) ignore the health risk of nitrate contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, pollution transport processes, and climate (Khader and McKee, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine where methemoglobinemia is the main health problem associated with the principal pollutant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not-use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs include healthcare for methemoglobinemia, purchase of bottled water, and installation and maintenance of the groundwater monitoring system. At current methemoglobinemia and bottled water costs of 150 $/person and 0.6 $/baby/day, the decision tree results show that the expected cost of establishing the proposed groundwater quality monitoring network exceeds the expected costs of the uninformed alternatives and there is not value to the information the monitoring system provides. However, the monitoring system will be preferred to ignoring the health risk or using alternative sources if the methemoglobinemia cost rises to 300 $/person or the bottled water cost increases to 2.3 $/baby/day. Similarly, the monitoring system has value if the system can more accurately report actual aquifer concentrations and the public more fully abides by managers' recommendations to use/not use the aquifer. The system also has value if it will serve a larger population or if its installation costs can be reduced, for example using a smaller number of monitoring wells. The VOI analysis shows how monitoring system design, accuracy, installation and operating costs, public awareness of health risks, costs of alternatives, and demographics together affect the value of implementing a system to monitor groundwater quality.

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

DOE PAGES

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo; ...

2017-07-14

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

Semi-Automated Approach for Mapping Urban Trees from Integrated Aerial LiDAR Point Cloud and Digital Imagery Datasets

NASA Astrophysics Data System (ADS)

Dogon-Yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-09-01

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

Forecasting the Performance of Agroforestry Systems

NASA Astrophysics Data System (ADS)

Luedeling, E.; Shepherd, K.

2014-12-01

Agroforestry has received considerable attention from scientists and development practitioners in recent years. It is recognized as a cornerstone of many traditional agricultural systems, as well as a new option for sustainable land management in currently treeless agricultural landscapes. Agroforestry systems are diverse, but most manifestations supply substantial ecosystem services, including marketable tree products, soil fertility, water cycle regulation, wildlife habitat and carbon sequestration. While these benefits have been well documented for many existing systems, projecting the outcomes of introducing new agroforestry systems, or forecasting system performance under changing environmental or climatic conditions, remains a substantial challenge. Due to the various interactions between system components, the multiple benefits produced by trees and crops, and the host of environmental, socioeconomic and cultural factors that shape agroforestry systems, mechanistic models of such systems quickly become very complex. They then require a lot of data for site-specific calibration, which presents a challenge for their use in new environmental and climatic domains, especially in data-scarce environments. For supporting decisions on the scaling up of agroforestry technologies, new projection methods are needed that can capture system complexity to an adequate degree, while taking full account of the fact that data on many system variables will virtually always be highly uncertain. This paper explores what projection methods are needed for supplying decision-makers with useful information on the performance of agroforestry in new places or new climates. Existing methods are discussed in light of these methodological needs. Finally, a participatory approach to performance projection is proposed that captures system dynamics in a holistic manner and makes probabilistic projections about expected system performance. This approach avoids the temptation to take spuriously precise model results at face value, and it is able to make predictions even where data is scarce. It thus provides a rapid and honest assessment option that can quickly supply decision-makers with system performance estimates, offering an opportunity to improve the targeting of agroforestry interventions.

Decision-Tree Program

NASA Technical Reports Server (NTRS)

Buntine, Wray

1994-01-01

IND computer program introduces Bayesian and Markov/maximum-likelihood (MML) methods and more-sophisticated methods of searching in growing trees. Produces more-accurate class-probability estimates important in applications like diagnosis. Provides range of features and styles with convenience for casual user, fine-tuning for advanced user or for those interested in research. Consists of four basic kinds of routines: data-manipulation, tree-generation, tree-testing, and tree-display. Written in C language.

Application of data mining techniques to explore predictors of upper urinary tract damage in patients with neurogenic bladder.

PubMed

Fang, H; Lu, B; Wang, X; Zheng, L; Sun, K; Cai, W

2017-08-17

This study proposed a decision tree model to screen upper urinary tract damage (UUTD) for patients with neurogenic bladder (NGB). Thirty-four NGB patients with UUTD were recruited in the case group, while 78 without UUTD were included in the control group. A decision tree method, classification and regression tree (CART), was then applied to develop the model in which UUTD was used as a dependent variable and history of urinary tract infections, bladder management, conservative treatment, and urodynamic findings were used as independent variables. The urethra function factor was found to be the primary screening information of patients and treated as the root node of the tree; Pabd max (maximum abdominal pressure, >14 cmH2O), Pves max (maximum intravesical pressure, ≤89 cmH2O), and gender (female) were also variables associated with UUTD. The accuracy of the proposed model was 84.8%, and the area under curve was 0.901 (95%CI=0.844-0.958), suggesting that the decision tree model might provide a new and convenient way to screen UUTD for NGB patients in both undeveloped and developing areas.

Graphic Representations as Tools for Decision Making.

ERIC Educational Resources Information Center

Howard, Judith

2001-01-01

Focuses on the use of graphic representations to enable students to improve their decision making skills in the social studies. Explores three visual aids used in assisting students with decision making: (1) the force field; (2) the decision tree; and (3) the decision making grid. (CMK)

Using data from monitoring combined sewer overflows to assess, improve, and maintain combined sewer systems.

PubMed

Montserrat, A; Bosch, Ll; Kiser, M A; Poch, M; Corominas, Ll

2015-02-01

Using low-cost sensors, data can be collected on the occurrence and duration of overflows in each combined sewer overflow (CSO) structure in a combined sewer system (CSS). The collection and analysis of real data can be used to assess, improve, and maintain CSSs in order to reduce the number and impact of overflows. The objective of this study was to develop a methodology to evaluate the performance of CSSs using low-cost monitoring. This methodology includes (1) assessing the capacity of a CSS using overflow duration and rain volume data, (2) characterizing the performance of CSO structures with statistics, (3) evaluating the compliance of a CSS with government guidelines, and (4) generating decision tree models to provide support to managers for making decisions about system maintenance. The methodology is demonstrated with a case study of a CSS in La Garriga, Spain. The rain volume breaking point from which CSO structures started to overflow ranged from 0.6 mm to 2.8 mm. The structures with the best and worst performance in terms of overflow (overflow probability, order, duration and CSO ranking) were characterized. Most of the obtained decision trees to predict overflows from rain data had accuracies ranging from 70% to 83%. The results obtained from the proposed methodology can greatly support managers and engineers dealing with real-world problems, improvements, and maintenance of CSSs. Copyright © 2014 Elsevier B.V. All rights reserved.

[Decision modeling for economic evaluation of health technologies].

PubMed

de Soárez, Patrícia Coelho; Soares, Marta Oliveira; Novaes, Hillegonda Maria Dutilh

2014-10-01

Most economic evaluations that participate in decision-making processes for incorporation and financing of technologies of health systems use decision models to assess the costs and benefits of the compared strategies. Despite the large number of economic evaluations conducted in Brazil, there is a pressing need to conduct an in-depth methodological study of the types of decision models and their applicability in our setting. The objective of this literature review is to contribute to the knowledge and use of decision models in the national context of economic evaluations of health technologies. This article presents general definitions about models and concerns with their use; it describes the main models: decision trees, Markov chains, micro-simulation, simulation of discrete and dynamic events; it discusses the elements involved in the choice of model; and exemplifies the models addressed in national economic evaluation studies of diagnostic and therapeutic preventive technologies and health programs.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

PubMed

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

The Effect of Defense R&D Expenditures on Military Capability and Technological Spillover

DTIC Science & Technology

2013-03-01

ix List of Figures Page Figure 1. Decision Tree for Sectoring R&D Units...approach, often called sectoring , categorizes R&D activities by funding source, and the functional approach categorizes R&D activities by their objective...economic objectives (defense, and control and care of environment) (OECD, 2002). Figure 1 shows the decision tree for sectoring R&D units and

Blood oxygen level dependent magnetic resonance imaging for detecting pathological patterns in lupus nephritis patients: a preliminary study using a decision tree model.

PubMed

Shi, Huilan; Jia, Junya; Li, Dong; Wei, Li; Shang, Wenya; Zheng, Zhenfeng

2018-02-09

Precise renal histopathological diagnosis will guide therapy strategy in patients with lupus nephritis. Blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) has been applicable noninvasive technique in renal disease. This current study was performed to explore whether BOLD MRI could contribute to diagnose renal pathological pattern. Adult patients with lupus nephritis renal pathological diagnosis were recruited for this study. Renal biopsy tissues were assessed based on the lupus nephritis ISN/RPS 2003 classification. The Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) was used to obtain functional magnetic resonance parameter, R2* values. Several functions of R2* values were calculated and used to construct algorithmic models for renal pathological patterns. In addition, the algorithmic models were compared as to their diagnostic capability. Both Histopathology and BOLD MRI were used to examine a total of twelve patients. Renal pathological patterns included five classes III (including 3 as class III + V) and seven classes IV (including 4 as class IV + V). Three algorithmic models, including decision tree, line discriminant, and logistic regression, were constructed to distinguish the renal pathological pattern of class III and class IV. The sensitivity of the decision tree model was better than that of the line discriminant model (71.87% vs 59.48%, P < 0.001) and inferior to that of the Logistic regression model (71.87% vs 78.71%, P < 0.001). The specificity of decision tree model was equivalent to that of the line discriminant model (63.87% vs 63.73%, P = 0.939) and higher than that of the logistic regression model (63.87% vs 38.0%, P < 0.001). The Area under the ROC curve (AUROCC) of the decision tree model was greater than that of the line discriminant model (0.765 vs 0.629, P < 0.001) and logistic regression model (0.765 vs 0.662, P < 0.001). BOLD MRI is a useful non-invasive imaging technique for the evaluation of lupus nephritis. Decision tree models constructed using functions of R2* values may facilitate the prediction of renal pathological patterns.

A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected With an Extended-Spectrum β-Lactamase-Producing Organism.

PubMed

Goodman, Katherine E; Lessler, Justin; Cosgrove, Sara E; Harris, Anthony D; Lautenbach, Ebbing; Han, Jennifer H; Milstone, Aaron M; Massey, Colin J; Tamma, Pranita D

2016-10-01

Timely identification of extended-spectrum β-lactamase (ESBL) bacteremia can improve clinical outcomes while minimizing unnecessary use of broad-spectrum antibiotics, including carbapenems. However, most clinical microbiology laboratories currently require at least 24 additional hours from the time of microbial genus and species identification to confirm ESBL production. Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL producing, to guide appropriate antibiotic therapy. We included patients ≥18 years of age with bacteremia due to Escherichia coli or Klebsiella species from October 2008 to March 2015 at Johns Hopkins Hospital. Isolates with ceftriaxone minimum inhibitory concentrations ≥2 µg/mL underwent ESBL confirmatory testing. Recursive partitioning was used to generate a decision tree to determine the likelihood that a bacteremic patient was infected with an ESBL producer. Discrimination of the original and cross-validated models was evaluated using receiver operating characteristic curves and by calculation of C-statistics. A total of 1288 patients with bacteremia met eligibility criteria. For 194 patients (15%), bacteremia was due to a confirmed ESBL producer. The final classification tree for predicting ESBL-positive bacteremia included 5 predictors: history of ESBL colonization/infection, chronic indwelling vascular hardware, age ≥43 years, recent hospitalization in an ESBL high-burden region, and ≥6 days of antibiotic exposure in the prior 6 months. The decision tree's positive and negative predictive values were 90.8% and 91.9%, respectively. Our findings suggest that a clinical decision tree can be used to estimate a bacteremic patient's likelihood of infection with ESBL-producing bacteria. Recursive partitioning offers a practical, user-friendly approach for addressing important diagnostic questions. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Ensemble stump classifiers and gene expression signatures in lung cancer.

PubMed

Frey, Lewis; Edgerton, Mary; Fisher, Douglas; Levy, Shawn

2007-01-01

Microarray data sets for cancer tumor tissue generally have very few samples, each sample having thousands of probes (i.e., continuous variables). The sparsity of samples makes it difficult for machine learning techniques to discover probes relevant to the classification of tumor tissue. By combining data from different platforms (i.e., data sources), data sparsity is reduced, but this typically requires normalizing data from the different platforms, which can be non-trivial. This paper proposes a variant on the idea of ensemble learners to circumvent the need for normalization. To facilitate comprehension we build ensembles of very simple classifiers known as decision stumps--decision trees of one test each. The Ensemble Stump Classifier (ESC) identifies an mRNA signature having three probes and high accuracy for distinguishing between adenocarcinoma and squamous cell carcinoma of the lung across four data sets. In terms of accuracy, ESC outperforms a decision tree classifier on all four data sets, outperforms ensemble decision trees on three data sets, and simple stump classifiers on two data sets.

Extensions and applications of ensemble-of-trees methods in machine learning

NASA Astrophysics Data System (ADS)

Bleich, Justin

Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) rely on algorithmic procedures to generate fits to data. In contrast, more recent ensemble techniques such as Bayesian Additive Regression Trees (BART) and Dynamic Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological advances for ensemble-of-trees techniques with an emphasis on the more recent Bayesian approaches. In particular, we focus on extensions of BART in four distinct ways. First, we develop a more robust implementation of BART for both research and application. We then develop a principled approach to variable selection for BART as well as the ability to naturally incorporate prior information on important covariates into the algorithm. Next, we propose a method for handling missing data that relies on the recursive structure of decision trees and does not require imputation. Last, we relax the assumption of homoskedasticity in the BART model to allow for parametric modeling of heteroskedasticity. The second part of this thesis returns to the classic algorithmic approaches in the context of classification problems with asymmetric costs of forecasting errors. First we consider the performance of RF and SGB more broadly and demonstrate its superiority to logistic regression for applications in criminology with asymmetric costs. Next, we use RF to forecast unplanned hospital readmissions upon patient discharge with asymmetric costs taken into account. Finally, we explore the construction of stable decision trees for forecasts of violence during probation hearings in court systems.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

PubMed

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

The risk of disabling, surgery and reoperation in Crohn's disease - A decision tree-based approach to prognosis.

PubMed

Dias, Cláudia Camila; Pereira Rodrigues, Pedro; Fernandes, Samuel; Portela, Francisco; Ministro, Paula; Martins, Diana; Sousa, Paula; Lago, Paula; Rosa, Isadora; Correia, Luis; Moura Santos, Paula; Magro, Fernando

2017-01-01

Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

NASA Astrophysics Data System (ADS)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-01

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Trees Are Terrific!

ERIC Educational Resources Information Center

Braus, Judy, Ed.

1992-01-01

Ranger Rick's NatureScope is a creative education series dedicated to inspiring in children an understanding and appreciation of the natural world while developing the skills they will need to make responsible decisions about the environment. Contents are organized into the following sections: (1) "What Makes a Tree a Tree?," including…

Forecasting Reading Anxiety for Promoting English-Language Reading Performance Based on Reading Annotation Behavior

ERIC Educational Resources Information Center

Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao

2016-01-01

To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

PubMed

Khosravi, Khabat; Pham, Binh Thai; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Tien Bui, Dieu

2018-06-15

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. Copyright © 2018 Elsevier B.V. All rights reserved.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

NASA Technical Reports Server (NTRS)

Otterstatter, Matthew R.

2005-01-01

The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

The use of decision trees and naïve Bayes algorithms and trace element patterns for controlling the authenticity of free-range-pastured hens' eggs.

PubMed

Barbosa, Rommel Melgaço; Nacano, Letícia Ramos; Freitas, Rodolfo; Batista, Bruno Lemos; Barbosa, Fernando

2014-09-01

This article aims to evaluate 2 machine learning algorithms, decision trees and naïve Bayes (NB), for egg classification (free-range eggs compared with battery eggs). The database used for the study consisted of 15 chemical elements (As, Ba, Cd, Co, Cs, Cu, Fe, Mg, Mn, Mo, Pb, Se, Sr, V, and Zn) determined in 52 eggs samples (20 free-range and 32 battery eggs) by inductively coupled plasma mass spectrometry. Our results demonstrated that decision trees and NB associated with the mineral contents of eggs provide a high level of accuracy (above 80% and 90%, respectively) for classification between free-range and battery eggs and can be used as an alternative method for adulteration evaluation. © 2014 Institute of Food Technologists®

Using real options analysis to support strategic management decisions

NASA Astrophysics Data System (ADS)

Kabaivanov, Stanimir; Markovska, Veneta; Milev, Mariyan

2013-12-01

Decision making is a complex process that requires taking into consideration multiple heterogeneous sources of uncertainty. Standard valuation and financial analysis techniques often fail to properly account for all these sources of risk as well as for all sources of additional flexibility. In this paper we explore applications of a modified binomial tree method for real options analysis (ROA) in an effort to improve decision making process. Usual cases of use of real options are analyzed with elaborate study on the applications and advantages that company management can derive from their application. A numeric results based on extending simple binomial tree approach for multiple sources of uncertainty are provided to demonstrate the improvement effects on management decisions.

Enhanced transpiration by riparian buffer trees in response to advection in a humid temperate agricultural landscape

USGS Publications Warehouse

Hernandez-Santana, V.; Asbjornsen, H.; Sauer, T.; Isenhart, T.; Schilling, K.; Schultz, Ronald

2011-01-01

Riparian buffers are designed as management practices to increase infiltration and reduce surface runoff and transport of sediment and nonpoint source pollutants from crop fields to adjacent streams. Achieving these ecosystem service goals depends, in part, on their ability to remove water from the soil via transpiration. In these systems, edges between crop fields and trees of the buffer systems can create advection processes, which could influence water use by trees. We conducted a field study in a riparian buffer system established in 1994 under a humid temperate climate, located in the Corn Belt region of the Midwestern U.S. (Iowa). The goals were to estimate stand level transpiration by the riparian buffer, quantify the controls on water use by the buffer system, and determine to what extent advective energy and tree position within the buffer system influence individual tree transpiration rates. We primarily focused on the water use response (determined with the Heat Ratio Method) of one of the dominant species (Acer saccharinum) and a subdominant (Juglans nigra). A few individuals of three additional species (Quercus bicolor, Betula nigra, Platanus occidentalis) were monitored over a shorter time period to assess the generality of responses. Meteorological stations were installed along a transect across the riparian buffer to determine the microclimate conditions. The differences found among individuals were attributed to differences in species sap velocities and sapwood depths, location relative to the forest edge and prevailing winds and canopy exposure and dominance. Sapflow rates for A. saccharinum trees growing at the SE edge (prevailing winds) were 39% greater than SE interior trees and 30% and 69% greater than NW interior and edge trees, respectively. No transpiration enhancement due to edge effect was detected in the subdominant J. nigra. The results were interpreted as indicative of advection effects from the surrounding crops. Further, significant differences were document in sapflow rates between the five study species, suggesting that selection of species is important for enhancing specific riparian buffer functions. However, more information is needed on water use patterns among diverse species growing under different climatic and biophysical conditions to assist policy and management decisions regarding effective buffer design. ?? 2011.

Knowledge Quality Functions for Rule Discovery

DTIC Science & Technology

1994-09-01

Managers in many organizations finding themselves in the possession of large and rapidly growing databases are beginning to suspect the information in their...missing values (Smyth and Goodman, 1992, p. 303). Decision trees "tend to grow very large for realistic applications and are thus difficult to interpret...by humans" (Holsheimer, 1994, p. 42). Decision trees also grow excessively complicated in the presence of noisy databases (Dhar and Tuzhilin, 1993, p

Structural Equation Model Trees

ERIC Educational Resources Information Center

Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman

2013-01-01

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree…

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm.

PubMed

Tayefi, Maryam; Tajfard, Mohammad; Saffar, Sara; Hanachi, Parichehr; Amirabadizadeh, Ali Reza; Esmaeily, Habibollah; Taghipour, Ali; Ferns, Gordon A; Moohebati, Mohsen; Ghayour-Mobarhan, Majid

2017-04-01

Coronary heart disease (CHD) is an important public health problem globally. Algorithms incorporating the assessment of clinical biomarkers together with several established traditional risk factors can help clinicians to predict CHD and support clinical decision making with respect to interventions. Decision tree (DT) is a data mining model for extracting hidden knowledge from large databases. We aimed to establish a predictive model for coronary heart disease using a decision tree algorithm. Here we used a dataset of 2346 individuals including 1159 healthy participants and 1187 participant who had undergone coronary angiography (405 participants with negative angiography and 782 participants with positive angiography). We entered 10 variables of a total 12 variables into the DT algorithm (including age, sex, FBG, TG, hs-CRP, TC, HDL, LDL, SBP and DBP). Our model could identify the associated risk factors of CHD with sensitivity, specificity, accuracy of 96%, 87%, 94% and respectively. Serum hs-CRP levels was at top of the tree in our model, following by FBG, gender and age. Our model appears to be an accurate, specific and sensitive model for identifying the presence of CHD, but will require validation in prospective studies. Copyright © 2017 Elsevier B.V. All rights reserved.

A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.

PubMed

Esmaily, Habibollah; Tayefi, Maryam; Doosti, Hassan; Ghayour-Mobarhan, Majid; Nezami, Hossein; Amirabadizadeh, Alireza

2018-04-24

We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. A cross-sectional study. The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM .

A combined NLP-differential evolution algorithm approach for the optimization of looped water distribution systems

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2011-08-01

This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.

Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems.

PubMed

Szlosek, Donald A; Ferrett, Jonathan

2016-01-01

As the number of clinical decision support systems (CDSSs) incorporated into electronic medical records (EMRs) increases, so does the need to evaluate their effectiveness. The use of medical record review and similar manual methods for evaluating decision rules is laborious and inefficient. The authors use machine learning and Natural Language Processing (NLP) algorithms to accurately evaluate a clinical decision support rule through an EMR system, and they compare it against manual evaluation. Modeled after the EMR system EPIC at Maine Medical Center, we developed a dummy data set containing physician notes in free text for 3,621 artificial patients records undergoing a head computed tomography (CT) scan for mild traumatic brain injury after the incorporation of an electronic best practice approach. We validated the accuracy of the Best Practice Advisories (BPA) using three machine learning algorithms-C-Support Vector Classification (SVC), Decision Tree Classifier (DecisionTreeClassifier), k-nearest neighbors classifier (KNeighborsClassifier)-by comparing their accuracy for adjudicating the occurrence of a mild traumatic brain injury against manual review. We then used the best of the three algorithms to evaluate the effectiveness of the BPA, and we compared the algorithm's evaluation of the BPA to that of manual review. The electronic best practice approach was found to have a sensitivity of 98.8 percent (96.83-100.0), specificity of 10.3 percent, PPV = 7.3 percent, and NPV = 99.2 percent when reviewed manually by abstractors. Though all the machine learning algorithms were observed to have a high level of prediction, the SVC displayed the highest with a sensitivity 93.33 percent (92.49-98.84), specificity of 97.62 percent (96.53-98.38), PPV = 50.00, NPV = 99.83. The SVC algorithm was observed to have a sensitivity of 97.9 percent (94.7-99.86), specificity 10.30 percent, PPV 7.25 percent, and NPV 99.2 percent for evaluating the best practice approach, after accounting for 17 cases (0.66 percent) where the patient records had to be reviewed manually due to the NPL systems inability to capture the proper diagnosis. CDSSs incorporated into EMRs can be evaluated in an automatic fashion by using NLP and machine learning techniques.

Environmental justice and factors that influence participation in tree planting programs in Portland, Oregon, U.S

Treesearch

Geoffrey H. Donovan; John Mills

2014-01-01

Many cities have policies encouraging homeowners to plant trees. For these policies to be effective, it is important to understand what motivates a homeowner’s tree-planting decision. Researchers address this question by identifying variables that influence participation in a tree-planting program in Portland, Oregon, U.S. According to the study, homeowners with street...

Intensive land use in the Swedish mountains between AD 800 and 1200 led to deforestation and ecosystem transformation with long-lasting effects.

PubMed

Östlund, Lars; Hörnberg, Greger; DeLuca, Thomas H; Liedgren, Lars; Wikström, Peder; Zackrisson, Olle; Josefsson, Torbjörn

2015-10-01

Anthropogenic deforestation has shaped ecosystems worldwide. In subarctic ecosystems, primarily inhabited by native peoples, deforestation is generally considered to be mainly associated with the industrial period. Here we examined mechanisms underlying deforestation a thousand years ago in a high-mountain valley with settlement artifacts located in subarctic Scandinavia. Using the Heureka Forestry Decision Support System, we modeled pre-settlement conditions and effects of tree cutting on forest cover. To examine lack of regeneration and present nutrient status, we analyzed soil nitrogen. We found that tree cutting could have deforested the valley within some hundred years. Overexploitation left the soil depleted beyond the capacity of re-establishment of trees. We suggest that pre-historical deforestation has occurred also in subarctic ecosystems and that ecosystem boundaries were especially vulnerable to this process. This study improves our understanding of mechanisms behind human-induced ecosystem transformations and tree-line changes, and of the concept of wilderness in the Scandinavian mountain range.

Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging.

PubMed

Yang, Cheng-Hong; Wu, Kuo-Chuan; Chuang, Li-Yeh; Chang, Hsueh-Wei

2018-01-01

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a r ibulose diphosphate carboxylase ( rbcL ) S NP b arcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree-selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.

Kernel and divergence techniques in high energy physics separations

NASA Astrophysics Data System (ADS)

Bouř, Petr; Kůs, Václav; Franc, Jiří

2017-10-01

Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at DØ experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82 % AUC while using the restricted feature selection entering the signal separation procedure.

A Clinical Decision Support System for Breast Cancer Patients

NASA Astrophysics Data System (ADS)

Fernandes, Ana S.; Alves, Pedro; Jarman, Ian H.; Etchells, Terence A.; Fonseca, José M.; Lisboa, Paulo J. G.

This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prognostic index that can be analysed together in order to obtain a more accurate prognostic assessment of the patient. Missing data is incorporated in the mentioned models, a common issue in medical data that was overcome using multiple imputation techniques. Risk group assignments are also provided through a methodology based on regression trees, where Boolean rules can be obtained expressed with patient characteristics.

Sediment source fingerprinting as an aid to catchment management: A review of the current state of knowledge and a methodological decision-tree for end-users

USGS Publications Warehouse

Collins, A.L; Pulley, S.; Foster, I.D.L; Gellis, Allen; Porto, P.; Horowitz, A.J.

2017-01-01

The growing awareness of the environmental significance of fine-grained sediment fluxes through catchment systems continues to underscore the need for reliable information on the principal sources of this material. Source estimates are difficult to obtain using traditional monitoring techniques, but sediment source fingerprinting or tracing procedures, have emerged as a potentially valuable alternative. Despite the rapidly increasing numbers of studies reporting the use of sediment source fingerprinting, several key challenges and uncertainties continue to hamper consensus among the international scientific community on key components of the existing methodological procedures. Accordingly, this contribution reviews and presents recent developments for several key aspects of fingerprinting, namely: sediment source classification, catchment source and target sediment sampling, tracer selection, grain size issues, tracer conservatism, source apportionment modelling, and assessment of source predictions using artificial mixtures. Finally, a decision-tree representing the current state of knowledge is presented, to guide end-users in applying the fingerprinting approach.

Multi-output decision trees for lesion segmentation in multiple sclerosis

NASA Astrophysics Data System (ADS)

Jog, Amod; Carass, Aaron; Pham, Dzung L.; Prince, Jerry L.

2015-03-01

Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

Re-Construction of Reference Population and Generating Weights by Decision Tree

DTIC Science & Technology

2017-07-21

2017 Claflin University Orangeburg, SC 29115 DEFENSE EQUAL OPPORTUNITY MANAGEMENT INSTITUTE RESEARCH, DEVELOPMENT, AND STRATEGIC...Original Dataset 32 List of Figures in Appendix B Figure 1: Flow and Components of Project 20 Figure 2: Decision Tree 31 Figure 3: Effects of Weight...can compare the sample data. The dataset of this project has the reference population on unit level for group and gender, which is in red-dotted box

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spatially filtered to enforce local consensus among neighboring pixels and the spatially filtered image is output.

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spacially filtered to enforce local consensus among neighboring pixels and the spacially filtered image is output.

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China.

PubMed

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-11-09

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities-for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals-were the main external sources of a large amount of Hg in the farmland soil.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

Identifying Risk and Protective Factors in Recidivist Juvenile Offenders: A Decision Tree Approach

PubMed Central

Ortega-Campos, Elena; García-García, Juan; Gil-Fenoy, Maria José; Zaldívar-Basurto, Flor

2016-01-01

Research on juvenile justice aims to identify profiles of risk and protective factors in juvenile offenders. This paper presents a study of profiles of risk factors that influence young offenders toward committing sanctionable antisocial behavior (S-ASB). Decision tree analysis is used as a multivariate approach to the phenomenon of repeated sanctionable antisocial behavior in juvenile offenders in Spain. The study sample was made up of the set of juveniles who were charged in a court case in the Juvenile Court of Almeria (Spain). The period of study of recidivism was two years from the baseline. The object of study is presented, through the implementation of a decision tree. Two profiles of risk and protective factors are found. Risk factors associated with higher rates of recidivism are antisocial peers, age at baseline S-ASB, problems in school and criminality in family members. PMID:27611313

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China

PubMed Central

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-01-01

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities—for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals—were the main external sources of a large amount of Hg in the farmland soil. PMID:27834884

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Using decision analysis to assess comparative clinical efficacy of surgical treatment of unstable ankle fractures.

PubMed

Michelson, James D

2013-11-01

The development of a robust treatment algorithm for ankle fractures based on well-established stability criteria has been shown to be prognostic with respect to treatment and outcomes. In parallel with the development of improved understanding of the biomechanical rationale of ankle fracture treatment has been an increased emphasis on assessing the effectiveness of medical and surgical interventions. The purpose of this study was to investigate the use of using decision analysis in the assessment of the cost effectiveness of operative treatment of ankle fractures based on the existing clinical data in the literature. Using the data obtained from a previous structured review of the ankle fracture literature, decision analysis trees were constructed using standard software. The decision nodes for the trees were based on ankle fracture stability criteria previously published. The outcomes were assessed by calculated Quality-Adjusted Life Years (QALYs) assigned to achieving normal ankle function, developing posttraumatic arthritis, or sustaining a postoperative infection. Sensitivity analysis was undertaken by varying the patient's age, incidence of arthritis, and incidence or infection. Decision analysis trees captured the essential aspects of clinical decision making in ankle fracture treatment in a clinically useful manner. In general, stable fractures yielded better outcomes with nonoperative treatment, whereas unstable fractures had better outcomes with surgery. These were consistent results over a wide range of postoperative infection rates. Varying the age of the patient did not qualitatively change the results. Between the ages of 30 and 80 years, surgery yielded higher expected QALYs than nonoperative care for unstable fractures, and generated lower QALYs than nonoperative care for stable fractures. Using local cost estimates for operative and nonoperative treatment, the incremental cost of surgery for unstable fractures was less than $40,000 per QALY (the usual cutoff for the determination of cost effectiveness) for patients aged up to 90 years. Decision analysis is a useful methodology in developing treatment guidelines. Numerous previous studies have indicated superior clinical outcomes when unstable ankle fractures underwent operative reduction and stabilization. What has been lacking was an examination of the cost effectiveness of such an approach, particularly in older patients who have fewer expected years of life. In light of the evidence for satisfactory outcomes for surgery of severe ankle fractures in older people, the justification for operative intervention is an obvious question that can be asked in the current increasingly cost-conscious environment. Using a decision-tree decision analysis structured around the stability-based ankle fracture classification system, in conjunction with a relatively simple cost effectiveness analysis, this study was able to demonstrate that surgical treatment of unstable ankle fractures in elderly patients is in fact cost effective. The clinical implication of the present analysis is that these existing treatment protocols for ankle fracture treatment are also cost effective when quality of life outcome measures are taken into account. Economic Level II. See Instructions for Authors for a complete description of levels of evidence.

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

NASA Astrophysics Data System (ADS)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

A decision support tool for identifying abuse of controlled substances by ForwardHealth Medicaid members.

PubMed

Mailloux, Allan T; Cummings, Stephen W; Mugdh, Mrinal

2010-01-01

Our objective was to use Wisconsin's Medicaid Evaluation and Decision Support (MEDS) data warehouse to develop and validate a decision support tool (DST) that (1) identifies Wisconsin Medicaid fee-for-service recipients who are abusing controlled substances, (2) effectively replicates clinical pharmacist recommendations for interventions intended to curb abuse of physician and pharmacy services, and (3) automates data extraction, profile generation and tracking of recommendations and interventions. From pharmacist manual reviews of medication profiles, seven measures of overutilization of controlled substances were developed, including (1-2) 6-month and 2-month "shopping" scores, (3-4) 6-month and 2-month forgery scores, (5) duplicate/same day prescriptions, (6) count of controlled substance claims, and the (7) shopping 6-month score for the individual therapeutic class with the highest score. The pattern analysis logic for the measures was encoded into SQL and applied to the medication profiles of 190 recipients who had already undergone manual review. The scores for each measure and numbers of providers were analyzed by exhaustive chi-squared automatic interaction detection (CHAID) to determine significant thresholds and combinations of predictors of pharmacist recommendations, resulting in a decision tree to classify recipients by pharmacist recommendations. The overall correct classification rate of the decision tree was 95.3%, with a 2.4% false positive rate and 4.0% false negative rate for lock-in versus prescriber-alert letter recommendations. Measures used by the decision tree include the 2-month and 6-month shopping scores, and the number of pharmacies and prescribers. The number of pharmacies was the best predictor of abuse of controlled substances. When a Medicaid recipient receives prescriptions for controlled substances at 8 or more pharmacies, the likelihood of a lock-in recommendation is 90%. The availability of the Wisconsin MEDS data warehouse has enabled development and application of a decision tree for detecting recipient fraud and abuse of controlled substance medications. Using standard pharmacy claims data, the decision tree effectively replicates pharmacist manual review recommendations. The DST has automated extraction and evaluation of pharmacy claims data for creating recommendations for guiding pharmacists in the selection of profiles for manual review. The DST is now the primary method used by the Wisconsin Medicaid program to detect fraud and abuse of physician and pharmacy services committed by recipients.

Under which conditions, additional monitoring data are worth gathering for improving decision making? Application of the VOI theory in the Bayesian Event Tree eruption forecasting framework

NASA Astrophysics Data System (ADS)

Loschetter, Annick; Rohmer, Jérémy

2016-04-01

Standard and new generation of monitoring observations provide in almost real-time important information about the evolution of the volcanic system. These observations are used to update the model and contribute to a better hazard assessment and to support decision making concerning potential evacuation. The framework BET_EF (based on Bayesian Event Tree) developed by INGV enables dealing with the integration of information from monitoring with the prospect of decision making. Using this framework, the objectives of the present work are i. to propose a method to assess the added value of information (within the Value Of Information (VOI) theory) from monitoring; ii. to perform sensitivity analysis on the different parameters that influence the VOI from monitoring. VOI consists in assessing the possible increase in expected value provided by gathering information, for instance through monitoring. Basically, the VOI is the difference between the value with information and the value without additional information in a Cost-Benefit approach. This theory is well suited to deal with situations that can be represented in the form of a decision tree such as the BET_EF tool. Reference values and ranges of variation (for sensitivity analysis) were defined for input parameters, based on data from the MESIMEX exercise (performed at Vesuvio volcano in 2006). Complementary methods for sensitivity analyses were implemented: local, global using Sobol' indices and regional using Contribution to Sample Mean and Variance plots. The results (specific to the case considered) obtained with the different techniques are in good agreement and enable answering the following questions: i. Which characteristics of monitoring are important for early warning (reliability)? ii. How do experts' opinions influence the hazard assessment and thus the decision? Concerning the characteristics of monitoring, the more influent parameters are the means rather than the variances for the case considered. For the parameters that concern expert setting, the weight attributed to monitoring measurement ω, the mean of thresholds, the economic context and the setting of the decision threshold are very influential. The interest of applying the VOI theory (more precisely the value of imperfect information) in the BET framework was demonstrated as support for helping experts in the setting of the monitoring system or for helping managers to decide the installation of additional monitoring systems. Acknowledgments: This work was carried out in the framework of the project MEDSUV. This project is funded under the call FP7 ENV.2012.6.4-2: Long-term monitoring experiment in geologically active regions of Europe prone to natural hazards: the Supersite concept. Grant agreement n°308665.

Defender-Attacker Decision Tree Analysis to Combat Terrorism.

PubMed

Garcia, Ryan J B; von Winterfeldt, Detlof

2016-12-01

We propose a methodology, called defender-attacker decision tree analysis, to evaluate defensive actions against terrorist attacks in a dynamic and hostile environment. Like most game-theoretic formulations of this problem, we assume that the defenders act rationally by maximizing their expected utility or minimizing their expected costs. However, we do not assume that attackers maximize their expected utilities. Instead, we encode the defender's limited knowledge about the attacker's motivations and capabilities as a conditional probability distribution over the attacker's decisions. We apply this methodology to the problem of defending against possible terrorist attacks on commercial airplanes, using one of three weapons: infrared-guided MANPADS (man-portable air defense systems), laser-guided MANPADS, or visually targeted RPGs (rocket propelled grenades). We also evaluate three countermeasures against these weapons: DIRCMs (directional infrared countermeasures), perimeter control around the airport, and hardening airplanes. The model includes deterrence effects, the effectiveness of the countermeasures, and the substitution of weapons and targets once a specific countermeasure is selected. It also includes a second stage of defensive decisions after an attack occurs. Key findings are: (1) due to the high cost of the countermeasures, not implementing countermeasures is the preferred defensive alternative for a large range of parameters; (2) if the probability of an attack and the associated consequences are large, a combination of DIRCMs and ground perimeter control are preferred over any single countermeasure. © 2016 Society for Risk Analysis.

Automated recommendation for cervical cancer screening and surveillance.

PubMed

Wagholikar, Kavishwar B; MacLaughlin, Kathy L; Casey, Petra M; Kastner, Thomas M; Henry, Michael R; Hankey, Ronald A; Peters, Steve G; Greenes, Robert A; Chute, Christopher G; Liu, Hongfang; Chaudhry, Rajeev

2014-01-01

Because of the complexity of cervical cancer prevention guidelines, clinicians often fail to follow best-practice recommendations. Moreover, existing clinical decision support (CDS) systems generally recommend a cervical cytology every three years for all female patients, which is inappropriate for patients with abnormal findings that require surveillance at shorter intervals. To address this problem, we developed a decision tree-based CDS system that integrates national guidelines to provide comprehensive guidance to clinicians. Validation was performed in several iterations by comparing recommendations generated by the system with those of clinicians for 333 patients. The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%. Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings. Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

A decision support system for prioritizing forested wetland restoration in the Yazoo Backwater Area, Mississippi

USGS Publications Warehouse

O'Hara, Charles G.; Davis, Angela A.; Kleiss, Barbara A.

2000-01-01

A working prototype decision support system (DSS) was developed for the Yazoo Backwater Area, Mississippi, to help planners and managers prioritize, plan, conduct, and optimize forested wetland restoration activities. The DSS comprises geographic information system (GIS) spatial data themes, application programs that provide a cumulative analysis of the relative ability of sites to function as wetlands, and output data that are specific to a given restoration analysis scenario. The DSS input includes GIS data themes such as geomorphology, soils, land use, elevation, farmed wetlands, flood frequency, topographic depressions, streams, public lands, roads, and permanent water bodies, which can be used as spatial templates to define areal hydrologic settings. These GIS data themes can then be ranked and combined to estimate the relative suitability of a potential wetland restoration site, thereby, determining relative wetland equivalence on the landscape. The GIS applications used in this DSS perform the following three functions: assess the ecology (the Eco-Assessor); reclassify land-use in areas selected for restoration (the Tree-Translator); and generate output data to compare restoration scenarios (the Parameter-Generator). Areas selected for reforestation are translated (in the GIS) into ?forested? land use, and the tree species that are ?planted? on the landscape (in the DSS) either compose an ecologically optimal or an economically optimal community of tree species. Output from the DSS can be compared and analyzed by using economic, statistical, graphical, and tabular methods. Output data for seven selected scenarios were generated for the Yazoo Backwater Area and are presented as examples to illustrate the flexibility of the DSS to identify areas that meet restoration objectives.

Classification of Liss IV Imagery Using Decision Tree Methods

NASA Astrophysics Data System (ADS)

Verma, Amit Kumar; Garg, P. K.; Prasad, K. S. Hari; Dadhwal, V. K.

2016-06-01

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

Diagnostic assessment of intraoperative cytology for papillary thyroid carcinoma: using a decision tree analysis.

PubMed

Pyo, J-S; Sohn, J H; Kang, G

2017-03-01

The aim of this study was to elucidate the cytological characteristics and the diagnostic usefulness of intraoperative cytology (IOC) for papillary thyroid carcinoma (PTC). In addition, using decision tree analysis, effective features for accurate cytological diagnosis were sought. We investigated cellularity, cytological features and diagnosis based on the Bethesda System for Reporting Thyroid Cytopathology in IOC of 240 conventional PTCs. The cytological features were evaluated in terms of nuclear score with nuclear features, and additional figures such as presence of swirling sheets, psammoma bodies, and multinucleated giant cells. The nuclear score (range 0-7) was made via seven nuclear features, including (1) enlarged, (2) oval or irregularly shaped nuclei, (3) longitudinal nuclear grooves, (4) intranuclear cytoplasmic pseudoinclusion, (5) pale nuclei with powdery chromatin, (6) nuclear membrane thickening, and (7) marginally placed micronucleoli. Nuclear scores in PTC, suspicious for malignancy, and atypia of undetermined significance cases were 6.18 ± 0.80, 4.48 ± 0.82, and 3.15 ± 0.67, respectively. Additional figures more frequent in PTC than in other diagnostic categories were identified. Cellularity of IOC significantly correlated with tumor size, nuclear score, and presence of additional figures. Also, IOCs with higher nuclear scores (4-7) significantly correlated with larger tumor size and presence of additional figures. In decision tree analysis, IOCs with nuclear score >5 and swirling sheets could be considered diagnostic for PTCs. Our study suggests that IOCs using nuclear features and additional figures could be useful with decreasing the likelihood of inconclusive results.

Development of an expert system for assessing trumpeter swan breeding habitat in the Northern Rocky Mountains.

USGS Publications Warehouse

Sojda, Richard S.; Cornely, John E.; Howe, Adele E.

2002-01-01

A decision support system for the management of the Rocky Mountain Population of Trumpeter Swans (Cygnus buccinators) is being developed. As part of this, three expert systems are also in development: one for assessing the quality of Trumpeter Swan breeding habitat; one for making water level recommendations in montane, palustrine wetlands; and one for assessing the contribution a particular site can make towards meeting objectives from as flyway perspective. The focus of this paper is the development of the breeding habitat expert system, which currently consists of 157 rules. Out purpose is to provide decision support for issues that appear to be beyond the capability of a single persons to conceptualize and solve. We propose that by involving multiple experts in the development and use of the systems, management will be significantly improved. The knowledge base for the expert system has been developed using standard knowledge engineering techniques with a small team of ecological experts. Knowledge was then coded using production rules organized in decision trees using a commercial expert system development shell. The final system has been deployed on the world wide web.

Including public-health benefits of trees in urban-forestry decision making

Treesearch

Geoffrey H. Donovan

2017-01-01

Research demonstrating the biophysical benefits of urban trees are often used to justify investments in urban forestry. Far less emphasis, however, is placed on the non-bio-physical benefits such as improvements in public health. Indeed, the public-health benefits of trees may be significantly larger than the biophysical benefits, and, therefore, failure to account for...

Goal Programming: A New Tool for the Christmas Tree Industry

Treesearch

Bruce G. Hansen

1977-01-01

Goal programing (GP) can be useful for decision making in the natural Christmas tree industry. Its usefulness is demonstrated through an analysis of a hypothetical problem in which two potential growers decide how to use 10 acres in growing Christmas trees. Though the physical settings are identical, distinct differences between their goals significantly influence the...

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

NASA Astrophysics Data System (ADS)

Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.; Guo, L.

2017-12-01

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. One of the main challenges of setting up such a system is finding appropriate model parameter values for ungauged catchments. Previous studies have shown that the transfer of parameter sets from hydrologically similar gauged catchments is one of the best performing regionalization methods. However, a remaining key issue is the identification of suitable descriptors of similarity. In this study, we use decision tree learning to explore parameter set transferability in the full space of catchment descriptors. For this purpose, a semi-distributed rainfall-runoff model is set up for 35 catchments in ten Chinese provinces. Hourly runoff data from in total 858 storm events are used to calibrate the model and to evaluate the performance of parameter set transfers between catchments. We then present a novel technique that uses the splitting rules of classification and regression trees (CART) for finding suitable donor catchments for ungauged target catchments. The ability of the model to detect flood events in assumed ungauged catchments is evaluated in series of leave-one-out tests. We show that CART analysis increases the probability of detection of 10-year flood events in comparison to a conventional measure of physiographic-climatic similarity by up to 20%. Decision tree learning can outperform other regionalization approaches because it generates rules that optimally consider spatial proximity and physical similarity. Spatial proximity can be used as a selection criteria but is skipped in the case where no similar gauged catchments are in the vicinity. We conclude that the CART regionalization concept is particularly suitable for implementation in sparsely gauged and topographically complex environments where a proximity-based regionalization concept is not applicable.

Decision tree analysis of treatment strategies for mild and moderate cases of clinical mastitis occurring in early lactation.

PubMed

Pinzón-Sánchez, C; Cabrera, V E; Ruegg, P L

2011-04-01

The objective of this study was to develop a decision tree to evaluate the economic impact of different durations of intramammary treatment for the first case of mild or moderate clinical mastitis (CM) occurring in early lactation with various scenarios of pathogen distributions and use of on-farm culture. The tree included 2 decision and 3 probability events. The first decision evaluated use of on-farm culture (OFC; 2 programs using OFC and 1 not using OFC) and the second decision evaluated treatment strategies (no intramammary antimicrobials or antimicrobials administered for 2, 5, or 8 d). The tree included probabilities for the distribution of etiologies (gram-positive, gram-negative, or no growth), bacteriological cure, and recurrence. The economic consequences of mastitis included costs of diagnosis and initial treatment, additional treatments, labor, discarded milk, milk production losses due to clinical and subclinical mastitis, culling, and transmission of infection to other cows (only for CM caused by Staphylococcus aureus). Pathogen-specific estimates for bacteriological cure and milk losses were used. The economically optimal path for several scenarios was determined by comparison of expected monetary values. For most scenarios, the optimal economic strategy was to treat CM caused by gram-positive pathogens for 2 d and to avoid antimicrobials for CM cases caused by gram-negative pathogens or when no pathogen was recovered. Use of extended intramammary antimicrobial therapy (5 or 8 d) resulted in the least expected monetary values. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Binary Decision Trees for Preoperative Periapical Cyst Screening Using Cone-beam Computed Tomography.

PubMed

Pitcher, Brandon; Alaqla, Ali; Noujeim, Marcel; Wealleans, James A; Kotsakis, Georgios; Chrepa, Vanessa

2017-03-01

Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm 3 , there was 80% probability of a cyst. If volume was <247 mm 3 and root displacement was present, cyst probability was 60% (78% accuracy). The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.

Automatic identification of inertial sensor placement on human body segments during walking

PubMed Central

2013-01-01

Background Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided. We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Methods Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). Results and conclusions After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method. PMID:23517757

Automatic identification of inertial sensor placement on human body segments during walking.

PubMed

Weenk, Dirk; van Beijnum, Bert-Jan F; Baten, Chris T M; Hermens, Hermie J; Veltink, Peter H

2013-03-21

Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically. Walking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis). After testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method.

Evaluation of cost-effectiveness from the funding body's point of view of ultrasound-guided central venous catheter insertion compared with the conventional technique.

PubMed

Noritomi, Danilo Teixeira; Zigaib, Rogério; Ranzani, Otavio T; Teich, Vanessa

2016-01-01

To evaluate the cost-effectiveness, from the funding body's point of view, of real-time ultrasound-guided central venous catheter insertion compared to the traditional method, which is based on the external anatomical landmark technique. A theoretical simulation based on international literature data was applied to the Brazilian context, i.e., the Unified Health System (Sistema Único de Saúde - SUS). A decision tree was constructed that showed the two central venous catheter insertion techniques: real-time ultrasonography versus external anatomical landmarks. The probabilities of failure and complications were extracted from a search on the PubMed and Embase databases, and values associated with the procedure and with complications were taken from market research and the Department of Information Technology of the Unified Health System (DATASUS). Each central venous catheter insertion alternative had a cost that could be calculated by following each of the possible paths on the decision tree. The incremental cost-effectiveness ratio was calculated by dividing the mean incremental cost of real-time ultrasound compared to the external anatomical landmark technique by the mean incremental benefit, in terms of avoided complications. When considering the incorporation of real-time ultrasound and the concomitant lower cost due to the reduced number of complications, the decision tree revealed a final mean cost for the external anatomical landmark technique of 262.27 Brazilian reals (R$) and for real-time ultrasound of R$187.94. The final incremental cost of the real-time ultrasound-guided technique was -R$74.33 per central venous catheter. The incremental cost-effectiveness ratio was -R$2,494.34 due to the pneumothorax avoided. Real-time ultrasound-guided central venous catheter insertion was associated with decreased failure and complication rates and hypothetically reduced costs from the view of the funding body, which in this case was the SUS.

Enabling the use of hereditary information from pedigree tools in medical knowledge-based systems.

PubMed

Gay, Pablo; López, Beatriz; Plà, Albert; Saperas, Jordi; Pous, Carles

2013-08-01

The use of family information is a key issue to deal with inheritance illnesses. This kind of information use to come in the form of pedigree files, which contain structured information as tree or graphs, which explains the family relationships. Knowledge-based systems should incorporate the information gathered by pedigree tools to assess medical decision making. In this paper, we propose a method to achieve such a goal, which consists on the definition of new indicators, and methods and rules to compute them from family trees. The method is illustrated with several case studies. We provide information about its implementation and integration on a case-based reasoning tool. The method has been experimentally tested with breast cancer diagnosis data. The results show the feasibility of our methodology. Copyright © 2013 Elsevier Inc. All rights reserved.

Application of Decision Tree to Obtain Optimal Operation Rules for Reservoir Flood Control Considering Sediment Desilting-Case Study of Tseng Wen Reservoir

NASA Astrophysics Data System (ADS)

ShiouWei, L.

2014-12-01

Reservoirs are the most important water resources facilities in Taiwan.However,due to the steep slope and fragile geological conditions in the mountain area,storm events usually cause serious debris flow and flood,and the flood then will flush large amount of sediment into reservoirs.The sedimentation caused by flood has great impact on the reservoirs life.Hence,how to operate a reservoir during flood events to increase the efficiency of sediment desilting without risk the reservoir safety and impact the water supply afterward is a crucial issue in Taiwan. 　Therefore,this study developed a novel optimization planning model for reservoir flood operation considering flood control and sediment desilting,and proposed easy to use operating rules represented by decision trees.The decision trees rules have considered flood mitigation,water supply and sediment desilting.The optimal planning model computes the optimal reservoir release for each flood event that minimum water supply impact and maximum sediment desilting without risk the reservoir safety.Beside the optimal flood operation planning model,this study also proposed decision tree based flood operating rules that were trained by the multiple optimal reservoir releases to synthesis flood scenarios.The synthesis flood scenarios consists of various synthesis storm events,reservoir's initial storage and target storages at the end of flood operating. 　Comparing the results operated by the decision tree operation rules(DTOR) with that by historical operation for Krosa Typhoon in 2007,the DTOR removed sediment 15.4% more than that of historical operation with reservoir storage only8.38×106m3 less than that of historical operation.For Jangmi Typhoon in 2008,the DTOR removed sediment 24.4% more than that of historical operation with reservoir storage only 7.58×106m3 less than that of historical operation.The results show that the proposed DTOR model can increase the sediment desilting efficiency and extend the reservoir life.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

PubMed

Erdoğan, Onur; Aydin Son, Yeşim

2014-01-01

Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection.

PubMed

Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

2017-06-12

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan-Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.

Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection †

PubMed Central

Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

2017-01-01

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan–Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities. PMID:28604628

Pricing and reimbursement frameworks in Central Eastern Europe: a decision tool to support choices.

PubMed

Kolasa, Katarzyna; Kalo, Zoltan; Hornby, Edward

2015-02-01

Given limited financial resources in the Central Eastern European (CEE) region, challenges in obtaining access to innovative medical technologies are formidable. The objective of this research was to develop a decision tree that supports decision makers and drug manufacturers from CEE region in their search for optimal innovative pricing and reimbursement scheme (IPRSs). A systematic literature review was performed to search for published IPRSs, and then ten experts from the CEE region were interviewed to ascertain their opinions on these schemes. In total, 33 articles representing 46 unique IPRSs were analyzed. Based on our literature review and subsequent expert input, key decision nodes and branches of the decision tree were developed. The results indicate that outcome-based schemes are better suited to deal with uncertainties surrounding cost effectiveness, while non-outcome-based schemes are more appropriate for pricing and budget impact challenges.

Development and Validation of a Primary Care-Based Family Health History and Decision Support Program (MeTree)

PubMed Central

Orlando, Lori A.; Buchanan, Adam H.; Hahn, Susan E.; Christianson, Carol A.; Powell, Karen P.; Skinner, Celette Sugg; Chesnut, Blair; Blach, Colette; Due, Barbara; Ginsburg, Geoffrey S.; Henrich, Vincent C.

2016-01-01

INTRODUCTION Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS Stakeholder feedback resulted in changes to MeTree’s interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree’s strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers’ needs. LIMITATIONS The tool was validated in a small cohort. CONCLUSION MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines. PMID:24044145

Belowground Microbiota and the Health of Tree Crops.

PubMed

Mercado-Blanco, Jesús; Abrantes, Isabel; Barra Caracciolo, Anna; Bevivino, Annamaria; Ciancio, Aurelio; Grenni, Paola; Hrynkiewicz, Katarzyna; Kredics, László; Proença, Diogo N

2018-01-01

Trees are crucial for sustaining life on our planet. Forests and land devoted to tree crops do not only supply essential edible products to humans and animals, but also additional goods such as paper or wood. They also prevent soil erosion, support microbial, animal, and plant biodiversity, play key roles in nutrient and water cycling processes, and mitigate the effects of climate change acting as carbon dioxide sinks. Hence, the health of forests and tree cropping systems is of particular significance. In particular, soil/rhizosphere/root-associated microbial communities (known as microbiota) are decisive to sustain the fitness, development, and productivity of trees. These benefits rely on processes aiming to enhance nutrient assimilation efficiency (plant growth promotion) and/or to protect against a number of (a)biotic constraints. Moreover, specific members of the microbial communities associated with perennial tree crops interact with soil invertebrate food webs, underpinning many density regulation mechanisms. This review discusses belowground microbiota interactions influencing the growth of tree crops. The study of tree-(micro)organism interactions taking place at the belowground level is crucial to understand how they contribute to processes like carbon sequestration, regulation of ecosystem functioning, and nutrient cycling. A comprehensive understanding of the relationship between roots and their associate microbiota can also facilitate the design of novel sustainable approaches for the benefit of these relevant agro-ecosystems. Here, we summarize the methodological approaches to unravel the composition and function of belowground microbiota, the factors influencing their interaction with tree crops, their benefits and harms, with a focus on representative examples of Biological Control Agents (BCA) used against relevant biotic constraints of tree crops. Finally, we add some concluding remarks and suggest future perspectives concerning the microbiota-assisted management strategies to sustain tree crops.

Belowground Microbiota and the Health of Tree Crops

PubMed Central

Mercado-Blanco, Jesús; Abrantes, Isabel; Barra Caracciolo, Anna; Bevivino, Annamaria; Ciancio, Aurelio; Grenni, Paola; Hrynkiewicz, Katarzyna; Kredics, László; Proença, Diogo N.

2018-01-01

Trees are crucial for sustaining life on our planet. Forests and land devoted to tree crops do not only supply essential edible products to humans and animals, but also additional goods such as paper or wood. They also prevent soil erosion, support microbial, animal, and plant biodiversity, play key roles in nutrient and water cycling processes, and mitigate the effects of climate change acting as carbon dioxide sinks. Hence, the health of forests and tree cropping systems is of particular significance. In particular, soil/rhizosphere/root-associated microbial communities (known as microbiota) are decisive to sustain the fitness, development, and productivity of trees. These benefits rely on processes aiming to enhance nutrient assimilation efficiency (plant growth promotion) and/or to protect against a number of (a)biotic constraints. Moreover, specific members of the microbial communities associated with perennial tree crops interact with soil invertebrate food webs, underpinning many density regulation mechanisms. This review discusses belowground microbiota interactions influencing the growth of tree crops. The study of tree-(micro)organism interactions taking place at the belowground level is crucial to understand how they contribute to processes like carbon sequestration, regulation of ecosystem functioning, and nutrient cycling. A comprehensive understanding of the relationship between roots and their associate microbiota can also facilitate the design of novel sustainable approaches for the benefit of these relevant agro-ecosystems. Here, we summarize the methodological approaches to unravel the composition and function of belowground microbiota, the factors influencing their interaction with tree crops, their benefits and harms, with a focus on representative examples of Biological Control Agents (BCA) used against relevant biotic constraints of tree crops. Finally, we add some concluding remarks and suggest future perspectives concerning the microbiota-assisted management strategies to sustain tree crops. PMID:29922245

Predicting post-fire tree mortality for 12 western US conifers using the First-Order Fire Effects Model (FOFEM)

Treesearch

Sharon Hood; Duncan Lutes

2017-01-01

Accurate prediction of fire-caused tree mortality is critical for making sound land management decisions such as developing burning prescriptions and post-fire management guidelines. To improve efforts to predict post-fire tree mortality, we developed 3-year post-fire mortality models for 12 Western conifer species - white fir (Abies concolor [Gord. &...

Context-Sensitive Ethics in School Psychology

ERIC Educational Resources Information Center

Lasser, Jon; Klose, Laurie McGarry; Robillard, Rachel

2013-01-01

Ethical codes and licensing rules provide foundational guidance for practicing school psychologists, but these sources fall short in their capacity to facilitate effective decision-making. When faced with ethical dilemmas, school psychologists can turn to decision-making models, but step-wise decision trees frequently lack the situation…

Branch: an interactive, web-based tool for testing hypotheses and developing predictive models.

PubMed

Gangavarapu, Karthik; Babji, Vyshakh; Meißner, Tobias; Su, Andrew I; Good, Benjamin M

2016-07-01

Branch is a web application that provides users with the ability to interact directly with large biomedical datasets. The interaction is mediated through a collaborative graphical user interface for building and evaluating decision trees. These trees can be used to compose and test sophisticated hypotheses and to develop predictive models. Decision trees are built and evaluated based on a library of imported datasets and can be stored in a collective area for sharing and re-use. Branch is hosted at http://biobranch.org/ and the open source code is available at http://bitbucket.org/sulab/biobranch/ asu@scripps.edu or bgood@scripps.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

PubMed Central

Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

2015-01-01

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Herowati, A. J.; Sugiharti, E.; Prasetiyo, B.

2018-03-01

A technique to dig valuable information buried or hidden in data collection which is so big to be found an interesting patterns that was previously unknown is called data mining. Data mining has been applied in the healthcare industry. One technique used data mining is classification. The decision tree included in the classification of data mining and algorithm developed by decision tree is C4.5 algorithm. A classifier is designed using applying pessimistic pruning in C4.5 algorithm in diagnosing chronic kidney disease. Pessimistic pruning use to identify and remove branches that are not needed, this is done to avoid overfitting the decision tree generated by the C4.5 algorithm. In this paper, the result obtained using these classifiers are presented and discussed. Using pessimistic pruning shows increase accuracy of C4.5 algorithm of 1.5% from 95% to 96.5% in diagnosing of chronic kidney disease.

The economic impact of pig-associated parasitic zoonosis in Northern Lao PDR.

PubMed

Choudhury, Adnan Ali Khan; Conlan, James V; Racloz, Vanessa Nadine; Reid, Simon Andrew; Blacksell, Stuart D; Fenwick, Stanley G; Thompson, Andrew R C; Khamlome, Boualam; Vongxay, Khamphouth; Whittaker, Maxine

2013-03-01

The parasitic zoonoses human cysticercosis (Taenia solium), taeniasis (other Taenia species) and trichinellosis (Trichinella species) are endemic in the Lao People's Democratic Republic (Lao PDR). This study was designed to quantify the economic burden pig-associated zoonotic disease pose in Lao PDR. In particular, the analysis included estimation of the losses in the pork industry as well as losses due to human illness and lost productivity. A Markov-probability based decision-tree model was chosen to form the basis of the calculations to estimate the economic and public health impacts of taeniasis, trichinellosis and cysticercosis. Two different decision trees were run simultaneously on the model's human cohort. A third decision tree simulated the potential impacts on pig production. The human capital method was used to estimate productivity loss. The results found varied significantly depending on the rate of hospitalisation due to neurocysticerosis. This study is the first systematic estimate of the economic impact of pig-associated zoonotic diseases in Lao PDR that demonstrates the significance of the diseases in that country.

Multi-Agent simulation of generation capacity expansion decisions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Botterud, A.; Mahalik, M.; Conzelmann, G.

2008-01-01

In this paper, we use a multi-agent simulation model, EMCAS, to analyze generation expansion in the Iberian electricity market. The expansion model simulates generation investment decisions of decentralized generating companies (GenCos) interacting in a complex, multidimensional environment. A probabilistic dispatch algorithm calculates prices and profits for new candidate units in different future states of the system. Uncertainties in future load, hydropower conditions, and competitorspsila actions are represented in a scenario tree, and decision analysis is used to identify the optimal expansion decision for each individual GenCo. We run the model using detailed data for the Iberian market. In a scenariomore » analysis, we look at the impact of market design variables, such as the energy price cap and carbon emission prices. We also analyze how market concentration and GenCospsila risk preferences influence the timing and choice of new generating capacity.« less

The Performance Analysis of the Map-Aided Fuzzy Decision Tree Based on the Pedestrian Dead Reckoning Algorithm in an Indoor Environment

PubMed Central

Chiang, Kai-Wei; Liao, Jhen-Kai; Tsai, Guang-Je; Chang, Hsiu-Wen

2015-01-01

Hardware sensors embedded in a smartphone allow the device to become an excellent mobile navigator. A smartphone is ideal for this task because its great international popularity has led to increased phone power and since most of the necessary infrastructure is already in place. However, using a smartphone for indoor pedestrian navigation can be problematic due to the low accuracy of sensors, imprecise predictability of pedestrian motion, and inaccessibility of the Global Navigation Satellite System (GNSS) in some indoor environments. Pedestrian Dead Reckoning (PDR) is one of the most common technologies used for pedestrian navigation, but in its present form, various errors tend to accumulate. This study introduces a fuzzy decision tree (FDT) aided by map information to improve the accuracy and stability of PDR with less dependency on infrastructure. First, the map is quickly surveyed by the Indoor Mobile Mapping System (IMMS). Next, Bluetooth beacons are implemented to enable the initializing of any position. Finally, map-aided FDT can estimate navigation solutions in real time. The experiments were conducted in different fields using a variety of smartphones and users in order to verify stability. The contrast PDR system demonstrates low stability for each case without pre-calibration and post-processing, but the proposed low-complexity FDT algorithm shows good stability and accuracy under the same conditions. PMID:26729114

Application of a hybrid generation/utility assessment heuristic to a class of scheduling problems

NASA Technical Reports Server (NTRS)

Heyward, Ann O.

1989-01-01

A two-stage heuristic solution approach for a class of multiobjective, n-job, 1-machine scheduling problems is described. Minimization of job-to-job interference for n jobs is sought. The first stage generates alternative schedule sequences by interchanging pairs of schedule elements. The set of alternative sequences can represent nodes of a decision tree; each node is reached via decision to interchange job elements. The second stage selects the parent node for the next generation of alternative sequences through automated paired comparison of objective performance for all current nodes. An application of the heuristic approach to communications satellite systems planning is presented.

Industrial and occupational ergonomics in the petrochemical process industry: a regression trees approach.

PubMed

Bevilacqua, M; Ciarapica, F E; Giacchetta, G

2008-07-01

This work is an attempt to apply classification tree methods to data regarding accidents in a medium-sized refinery, so as to identify the important relationships between the variables, which can be considered as decision-making rules when adopting any measures for improvement. The results obtained using the CART (Classification And Regression Trees) method proved to be the most precise and, in general, they are encouraging concerning the use of tree diagrams as preliminary explorative techniques for the assessment of the ergonomic, management and operational parameters which influence high accident risk situations. The Occupational Injury analysis carried out in this paper was planned as a dynamic process and can be repeated systematically. The CART technique, which considers a very wide set of objective and predictive variables, shows new cause-effect correlations in occupational safety which had never been previously described, highlighting possible injury risk groups and supporting decision-making in these areas. The use of classification trees must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into traditional types of analysis.

A research of selected textural features for detection of asbestos-cement roofing sheets using orthoimages

NASA Astrophysics Data System (ADS)

Książek, Judyta

2015-10-01

At present, there has been a great interest in the development of texture based image classification methods in many different areas. This study presents the results of research carried out to assess the usefulness of selected textural features for detection of asbestos-cement roofs in orthophotomap classification. Two different orthophotomaps of southern Poland (with ground resolution: 5 cm and 25 cm) were used. On both orthoimages representative samples for two classes: asbestos-cement roofing sheets and other roofing materials were selected. Estimation of texture analysis usefulness was conducted using machine learning methods based on decision trees (C5.0 algorithm). For this purpose, various sets of texture parameters were calculated in MaZda software. During the calculation of decision trees different numbers of texture parameters groups were considered. In order to obtain the best settings for decision trees models cross-validation was performed. Decision trees models with the lowest mean classification error were selected. The accuracy of the classification was held based on validation data sets, which were not used for the classification learning. For 5 cm ground resolution samples, the lowest mean classification error was 15.6%. The lowest mean classification error in the case of 25 cm ground resolution was 20.0%. The obtained results confirm potential usefulness of the texture parameter image processing for detection of asbestos-cement roofing sheets. In order to improve the accuracy another extended study should be considered in which additional textural features as well as spectral characteristics should be analyzed.

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

PubMed

Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

2014-12-01

This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer.

PubMed

Miles, Kenneth A; Ganeshan, Balaji; Rodriguez-Justo, Manuel; Goh, Vicky J; Ziauddin, Zia; Engledow, Alec; Meagher, Marie; Endozo, Raymondo; Taylor, Stuart A; Halligan, Stephen; Ell, Peter J; Groves, Ashley M

2014-03-01

This study explores the potential for multifunctional imaging to provide a signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer. This prospective study approved by the institutional review board comprised 33 patients undergoing PET/CT before surgery for proven primary colorectal cancer. Tumor tissue was examined histologically for presence of the KRAS mutations and for expression of hypoxia-inducible factor-1 (HIF-1) and minichromosome maintenance protein 2 (mcm2). The following imaging parameters were derived for each tumor: (18)F-FDG uptake ((18)F-FDG maximum standardized uptake value [SUVmax]), CT texture (expressed as mean of positive pixels [MPP]), and blood flow measured by dynamic contrast-enhanced CT. A recursive decision tree was developed in which the imaging investigations were applied sequentially to identify tumors with KRAS mutations. Monte Carlo analysis provided mean values and 95% confidence intervals for sensitivity, specificity, and accuracy. The final decision tree comprised 4 decision nodes and 5 terminal nodes, 2 of which identified KRAS mutants. The true-positive rate, false-positive rate, and accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%-93.9%), 0% (0%-10.4%), and 90.1% (79.2%-96.0%), respectively. KRAS mutants with high (18)F-FDG SUVmax and low MPP showed greater frequency of HIF-1 expression (P = 0.032). KRAS mutants with low (18)F-FDG SUV(max), high MPP, and high blood flow expressed mcm2 (P = 0.036). Multifunctional imaging with PET/CT and recursive decision-tree analysis to combine measurements of tumor (18)F-FDG uptake, CT texture, and perfusion has the potential to identify imaging signatures for colorectal cancers with KRAS mutations exhibiting hypoxic or proliferative phenotypes.

Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

PubMed

Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

2012-01-01

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

Insurance Contract Analysis for Company Decision Support in Acquisition Management

NASA Astrophysics Data System (ADS)

Chernovita, H. P.; Manongga, D.; Iriani, A.

2017-01-01

One of company activities to retain their business is marketing the products which include in acquisition management to get new customers. Insurance contract analysis using ID3 to produce decision tree and rules to be decision support for the insurance company. The decision tree shows 13 rules that lead to contract termination claim. This could be a guide for the insurance company in acquisition management to prevent contract binding with these contract condition because it has a big chance for the customer to terminate their insurance contract before its expired date. As the result, there are several strong points that could be the determinant of contract termination such as: 1) customer age whether too young or too old, 2) long insurance period (above 10 years), 3) big insurance amount, 4) big amount of premium charges, and 5) payment method.

Efficient discovery of risk patterns in medical data.

PubMed

Li, Jiuyong; Fu, Ada Wai-chee; Fahey, Paul

2009-01-01

This paper studies a problem of efficiently discovering risk patterns in medical data. Risk patterns are defined by a statistical metric, relative risk, which has been widely used in epidemiological research. To avoid fruitless search in the complete exploration of risk patterns, we define optimal risk pattern set to exclude superfluous patterns, i.e. complicated patterns with lower relative risk than their corresponding simpler form patterns. We prove that mining optimal risk pattern sets conforms an anti-monotone property that supports an efficient mining algorithm. We propose an efficient algorithm for mining optimal risk pattern sets based on this property. We also propose a hierarchical structure to present discovered patterns for the easy perusal by domain experts. The proposed approach is compared with two well-known rule discovery methods, decision tree and association rule mining approaches on benchmark data sets and applied to a real world application. The proposed method discovers more and better quality risk patterns than a decision tree approach. The decision tree method is not designed for such applications and is inadequate for pattern exploring. The proposed method does not discover a large number of uninteresting superfluous patterns as an association mining approach does. The proposed method is more efficient than an association rule mining method. A real world case study shows that the method reveals some interesting risk patterns to medical practitioners. The proposed method is an efficient approach to explore risk patterns. It quickly identifies cohorts of patients that are vulnerable to a risk outcome from a large data set. The proposed method is useful for exploratory study on large medical data to generate and refine hypotheses. The method is also useful for designing medical surveillance systems.

Comparative seed-tree and selection harvesting costs in young-growth mixed-conifer stands

Treesearch

William A. Atkinson; Dale O. Hall

1963-01-01

Little difference was found between yarding and felling costs in seed-tree and selection harvest cuts. The volume per acre logged was 23,800 board feet on the seed-tree compartments and 10,600 board feet on the selection compartments. For a comparable operation with this range of volumes, cutting method decisions should be based on factors other than logging costs....

Merger of three modeling approaches to assess potential effects of climate change on trees in the eastern United States

Treesearch

Louis R. Iverson; Anantha M. Prasad; Stephen N. Matthews; Matthew P. Peters

2010-01-01

Climate change will likely cause impacts that are species specific and significant; modeling is critical to better understand potential changes in suitable habitat. We use empirical, abundance-based habitat models utilizing decision tree-based ensemble methods to explore potential changes of 134 tree species habitats in the eastern United States (http://www.nrs.fs.fed....

Temporal Heterogeneity and the Value of Slowness in Robotic Systems

DTIC Science & Technology

2015-11-01

DIMENSIONS OF HETEROGENEITY By now, we have become reasonably good at designing distributed control strategies for teams of networked agents in order...possible is the recent emergence of a relatively mature theory of how to coordinate control decisions across teams of networked agents. In fact...Loris, illustrated in Figure 2. Figure 2: Slow mammals that serve as bio-inspiration for SlowBot Behavior [Wikipedia] Top: Tree

Automatic energy expenditure measurement for health science.

PubMed

Catal, Cagatay; Akbulut, Akhan

2018-04-01

It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results. Copyright © 2018 Elsevier B.V. All rights reserved.

Landsat-derived cropland mask for Tanzania using 2010-2013 time series and decision tree classifier methods

NASA Astrophysics Data System (ADS)

Justice, C. J.

2015-12-01

80% of Tanzania's population is involved in the agriculture sector. Despite this national dependence, agricultural reporting is minimal and monitoring efforts are in their infancy. The cropland mask developed through this study provides the framework for agricultural monitoring through informing analysis of crop conditions, dispersion, and intensity at a national scale. Tanzania is dominated by smallholder agricultural systems with an average field size of less than one hectare (Sarris et al, 2006). At this field scale, previous classifications of agricultural land in Tanzania using MODIS course resolution data are insufficient to inform a working monitoring system. The nation-wide cropland mask in this study was developed using composited Landsat tiles from a 2010-2013 time series. Decision tree classifiers methods were used in the study with representative training areas collected for agriculture and no agriculture using appropriate indices to separate these classes (Hansen et al, 2013). Validation was done using random sample and high resolution satellite images to compare Agriculture and No agriculture samples from the study area. The techniques used in this study were successful and have the potential to be adapted for other countries, allowing targeted monitoring efforts to improve food security, market price, and inform agricultural policy.

Sediment source fingerprinting as an aid to catchment management: A review of the current state of knowledge and a methodological decision-tree for end-users.

PubMed

Collins, A L; Pulley, S; Foster, I D L; Gellis, A; Porto, P; Horowitz, A J

2017-06-01

The growing awareness of the environmental significance of fine-grained sediment fluxes through catchment systems continues to underscore the need for reliable information on the principal sources of this material. Source estimates are difficult to obtain using traditional monitoring techniques, but sediment source fingerprinting or tracing procedures, have emerged as a potentially valuable alternative. Despite the rapidly increasing numbers of studies reporting the use of sediment source fingerprinting, several key challenges and uncertainties continue to hamper consensus among the international scientific community on key components of the existing methodological procedures. Accordingly, this contribution reviews and presents recent developments for several key aspects of fingerprinting, namely: sediment source classification, catchment source and target sediment sampling, tracer selection, grain size issues, tracer conservatism, source apportionment modelling, and assessment of source predictions using artificial mixtures. Finally, a decision-tree representing the current state of knowledge is presented, to guide end-users in applying the fingerprinting approach. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Ecosystem Services and Biodiversity in a Rapidly Transforming Landscape in Northern Borneo.

PubMed

Labrière, Nicolas; Laumonier, Yves; Locatelli, Bruno; Vieilledent, Ghislain; Comptour, Marion

2015-01-01

Because industrial agriculture keeps expanding in Southeast Asia at the expense of natural forests and traditional swidden systems, comparing biodiversity and ecosystem services in the traditional forest-swidden agriculture system vs. monocultures is needed to guide decision making on land-use planning. Focusing on tree diversity, soil erosion control, and climate change mitigation through carbon storage, we surveyed vegetation and monitored soil loss in various land-use areas in a northern Bornean agricultural landscape shaped by swidden agriculture, rubber tapping, and logging, where various levels and types of disturbance have created a fine mosaic of vegetation from food crop fields to natural forest. Tree species diversity and ecosystem service production were highest in natural forests. Logged-over forests produced services similar to those of natural forests. Land uses related to the swidden agriculture system largely outperformed oil palm or rubber monocultures in terms of tree species diversity and service production. Natural and logged-over forests should be maintained or managed as integral parts of the swidden system, and landscape multifunctionality should be sustained. Because natural forests host a unique diversity of trees and produce high levels of ecosystem services, targeting carbon stock protection, e.g. through financial mechanisms such as Reducing Emissions from Deforestation and Forest Degradation (REDD+), will synergistically provide benefits for biodiversity and a wide range of other services. However, the way such mechanisms could benefit communities must be carefully evaluated to counter the high opportunity cost of conversion to monocultures that might generate greater income, but would be detrimental to the production of multiple ecosystem services.

Ecosystem Services and Biodiversity in a Rapidly Transforming Landscape in Northern Borneo

PubMed Central

Labrière, Nicolas; Laumonier, Yves; Locatelli, Bruno; Vieilledent, Ghislain; Comptour, Marion

2015-01-01

Because industrial agriculture keeps expanding in Southeast Asia at the expense of natural forests and traditional swidden systems, comparing biodiversity and ecosystem services in the traditional forest–swidden agriculture system vs. monocultures is needed to guide decision making on land-use planning. Focusing on tree diversity, soil erosion control, and climate change mitigation through carbon storage, we surveyed vegetation and monitored soil loss in various land-use areas in a northern Bornean agricultural landscape shaped by swidden agriculture, rubber tapping, and logging, where various levels and types of disturbance have created a fine mosaic of vegetation from food crop fields to natural forest. Tree species diversity and ecosystem service production were highest in natural forests. Logged-over forests produced services similar to those of natural forests. Land uses related to the swidden agriculture system largely outperformed oil palm or rubber monocultures in terms of tree species diversity and service production. Natural and logged-over forests should be maintained or managed as integral parts of the swidden system, and landscape multifunctionality should be sustained. Because natural forests host a unique diversity of trees and produce high levels of ecosystem services, targeting carbon stock protection, e.g. through financial mechanisms such as Reducing Emissions from Deforestation and Forest Degradation (REDD+), will synergistically provide benefits for biodiversity and a wide range of other services. However, the way such mechanisms could benefit communities must be carefully evaluated to counter the high opportunity cost of conversion to monocultures that might generate greater income, but would be detrimental to the production of multiple ecosystem services. PMID:26466120

IDHEAS – A NEW APPROACH FOR HUMAN RELIABILITY ANALYSIS

DOE Office of Scientific and Technical Information (OSTI.GOV)

G. W. Parry; J.A Forester; V.N. Dang

2013-09-01

This paper describes a method, IDHEAS (Integrated Decision-Tree Human Event Analysis System) that has been developed jointly by the US NRC and EPRI as an improved approach to Human Reliability Analysis (HRA) that is based on an understanding of the cognitive mechanisms and performance influencing factors (PIFs) that affect operator responses. The paper describes the various elements of the method, namely the performance of a detailed cognitive task analysis that is documented in a crew response tree (CRT), and the development of the associated time-line to identify the critical tasks, i.e. those whose failure results in a human failure eventmore » (HFE), and an approach to quantification that is based on explanations of why the HFE might occur.« less

Communications and tracking expert systems study

NASA Technical Reports Server (NTRS)

Leibfried, T. F.; Feagin, Terry; Overland, David

1987-01-01

The original objectives of the study consisted of five broad areas of investigation: criteria and issues for explanation of communication and tracking system anomaly detection, isolation, and recovery; data storage simplification issues for fault detection expert systems; data selection procedures for decision tree pruning and optimization to enhance the abstraction of pertinent information for clear explanation; criteria for establishing levels of explanation suited to needs; and analysis of expert system interaction and modularization. Progress was made in all areas, but to a lesser extent in the criteria for establishing levels of explanation suited to needs. Among the types of expert systems studied were those related to anomaly or fault detection, isolation, and recovery.

Developing an Environmental Decision Support System for Stream Management: the STREAMES Experience

NASA Astrophysics Data System (ADS)

Riera, J.; Argerich, A.; Comas, J.; Llorens, E.; Martí, E.; Godé, L.; Pargament, D.; Puig, M.; Sabater, F.

2005-05-01

Transferring research knowledge to stream managers is crucial for scientifically sound management. Environmental decision support systems are advocated as an effective means to accomplish this. STREAMES (STream REAach Management: an Expert System) is a decision tree based EDSS prototype developed within the context of an European project as a tool to assist water managers in the diagnosis of problems, detection of causes, and selection of management strategies for coping with stream degradation issues related mostly to excess nutrient availability. STREAMES was developed by a team of scientists, water managers, and experts in knowledge engineering. Although the tool focuses on management at the stream reach scale, it also incorporates a mass-balance catchment nutrient emission model and a simple GIS module. We will briefly present the prototype and share our experience in its development. Emphasis will be placed on the process of knowledge acquisition, the design process, the pitfalls and benefits of the communication between scientists and managers, and the potential for future development of STREAMES, particularly in the context of the EU Water Framework Directive.

Tree-to-tree variation in seed size and its consequences for seed dispersal versus predation by rodents.

PubMed

Wang, Bo; Ives, Anthony R

2017-03-01

Individual variation in seed size and seed production is high in many plant species. How does this variation affect seed-dispersing animals and, in turn, the fitness of individual plants? In this study, we first surveyed intraspecific variation in seed mass and production in a population of a Chinese white pine, Pinus armandii. For 134 target trees investigated in 2012, there was very high variation in seed size, with mean seed mass varying among trees almost tenfold, from 0.038 to 0.361 g. Furthermore, 30 of the 134 trees produced seeds 2 years later, and for these individuals there was a correlation in seed mass of 0.59 between years, implying consistent differences among individuals. For a subset of 67 trees, we monitored the foraging preferences of scatter-hoarding rodents on a total of 15,301 seeds: 8380 were ignored, 3184 were eaten in situ, 2651 were eaten after being cached, and 395 were successfully dispersed (cached and left intact). At the scale of individual seeds, seed mass affected almost every decision that rodents made to eat, remove, and cache individual seeds. At the level of individual trees, larger seeds had increased probabilities of both predation and successful dispersal: the effects of mean seed size on costs (predation) and benefits (caching) balanced out. Thus, despite seed size affecting rodent decisions, variation among trees in dispersal success associated with mean seed size was small once seeds were harvested. This might explain, at least in part, the maintenance of high variation in mean seed mass among tree individuals.

How trees allocate carbon for optimal growth: insight from a game-theoretic model.

PubMed

Fu, Liyong; Sun, Lidan; Han, Hao; Jiang, Libo; Zhu, Sheng; Ye, Meixia; Tang, Shouzheng; Huang, Minren; Wu, Rongling

2017-02-01

How trees allocate photosynthetic products to primary height growth and secondary radial growth reflects their capacity to best use environmental resources. Despite substantial efforts to explore tree height-diameter relationship empirically and through theoretical modeling, our understanding of the biological mechanisms that govern this phenomenon is still limited. By thinking of stem woody biomass production as an ecological system of apical and lateral growth components, we implement game theory to model and discern how these two components cooperate symbiotically with each other or compete for resources to determine the size of a tree stem. This resulting allometry game theory is further embedded within a genetic mapping and association paradigm, allowing the genetic loci mediating the carbon allocation of stemwood growth to be characterized and mapped throughout the genome. Allometry game theory was validated by analyzing a mapping data of stem height and diameter growth over perennial seasons in a poplar tree. Several key quantitative trait loci were found to interpret the process and pattern of stemwood growth through regulating the ecological interactions of stem apical and lateral growth. The application of allometry game theory enables the prediction of the situations in which the cooperation, competition or altruism is an optimal decision of a tree to fully use the environmental resources it owns. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Systems Maintenance Automated Repair Tasks (SMART)

NASA Technical Reports Server (NTRS)

Schuh, Joseph; Mitchell, Brent; Locklear, Louis; Belson, Martin A.; Al-Shihabi, Mary Jo Y.; King, Nadean; Norena, Elkin; Hardin, Derek

2010-01-01

SMART is a uniform automated discrepancy analysis and repair-authoring platform that improves technical accuracy and timely delivery of repair procedures for a given discrepancy (see figure a). SMART will minimize data errors, create uniform repair processes, and enhance the existing knowledge base of engineering repair processes. This innovation is the first tool developed that links the hardware specification requirements with the actual repair methods, sequences, and required equipment. SMART is flexibly designed to be useable by multiple engineering groups requiring decision analysis, and by any work authorization and disposition platform (see figure b). The organizational logic creates the link between specification requirements of the hardware, and specific procedures required to repair discrepancies. The first segment in the SMART process uses a decision analysis tree to define all the permutations between component/ subcomponent/discrepancy/repair on the hardware. The second segment uses a repair matrix to define what the steps and sequences are for any repair defined in the decision tree. This segment also allows for the selection of specific steps from multivariable steps. SMART will also be able to interface with outside databases and to store information from them to be inserted into the repair-procedure document. Some of the steps will be identified as optional, and would only be used based on the location and the current configuration of the hardware. The output from this analysis would be sent to a work authoring system in the form of a predefined sequence of steps containing required actions, tools, parts, materials, certifications, and specific requirements controlling quality, functional requirements, and limitations.

Human Mars Mission Design - The Ultimate Systems Challenge

NASA Technical Reports Server (NTRS)

Connolly, John F.; Joosten, B. Kent; Drake, Bret; Hoffman, Steve; Polsgrove, Tara; Rucker, Michelle; Andrews, Alida; Williams, Nehemiah

2017-01-01

A human mission to Mars will occur at some time in the coming decades. When it does, it will be the end result of a complex network of interconnected design choices, systems analyses, technical optimizations, and non-technical compromises. This mission will extend the technologies, engineering design, and systems analyses to new limits, and may very well be the most complex undertaking in human history. It can be illustrated as a large menu, or as a large decision tree. Whatever the visualization tool, there are numerous design decisions required to assemble a human Mars mission, and many of these interconnect with one another. This paper examines these many decisions and further details a number of choices that are highly interwoven throughout the mission design. The large quantity of variables and their interconnectedness results in a highly complex systems challenge, and the paper illustrates how a change in one variable results in ripples (sometimes unintended) throughout many other facets of the design. The paper concludes with a discussion of some mission design variables that can be addressed first, and those that have already been addressed as a result of ongoing National Aeronautics and Space Administration (NASA) developments, or as a result of decisions outside the technical arena. It advocates the need for a 'reference design' that can be used as a point of comparison, and to illustrate the system-wide impacts as design variables change.

Forestry 101.

ERIC Educational Resources Information Center

Markham, Mary T.

2000-01-01

Introduces a unit on forest management in which students manage the school forest. Involves students in tree identification, determining the size or volume and height of trees, and evaluation of the forest for management decisions. Integrates mathematics, writing, and social studies with plant classification, plant reproduction, and the use of…

Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation. Volume 1: Study results

NASA Technical Reports Server (NTRS)

Lowrie, J. W.; Fermelia, A. J.; Haley, D. C.; Gremban, K. D.; Vanbaalen, J.; Walsh, R. W.

1982-01-01

A variety of artificial intelligence techniques which could be used with regard to NASA space applications and robotics were evaluated. The techniques studied were decision tree manipulators, problem solvers, rule based systems, logic programming languages, representation language languages, and expert systems. The overall structure of a robotic simulation tool was defined and a framework for that tool developed. Nonlinear and linearized dynamics equations were formulated for n link manipulator configurations. A framework for the robotic simulation was established which uses validated manipulator component models connected according to a user defined configuration.

Interactions between factors related to the decision of sex offenders to confess during police interrogation: a classification-tree approach.

PubMed

Beauregard, Eric; Deslauriers-Varin, Nadine; St-Yves, Michel

2010-09-01

Most studies of confessions have looked at the influence of individual factors, neglecting the potential interactions between these factors and their impact on the decision to confess or not during an interrogation. Classification and regression tree analyses conducted on a sample of 624 convicted sex offenders showed that certain factors related to the offenders (e.g., personality, criminal career), victims (e.g., sex, relationship to offender), and case (e.g., time of day of the crime) were related to the decision to confess or not during the police interrogation. Several interactions were also observed between these factors. Results will be discussed in light of previous findings and interrogation strategies for sex offenders.

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

PubMed

Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: The Advance Value Framework.

PubMed

Angelis, Aris; Kanavos, Panos

2017-09-01

Escalating drug prices have catalysed the generation of numerous "value frameworks" with the aim of informing payers, clinicians and patients on the assessment and appraisal process of new medicines for the purpose of coverage and treatment selection decisions. Although this is an important step towards a more inclusive Value Based Assessment (VBA) approach, aspects of these frameworks are based on weak methodologies and could potentially result in misleading recommendations or decisions. In this paper, a Multiple Criteria Decision Analysis (MCDA) methodological process, based on Multi Attribute Value Theory (MAVT), is adopted for building a multi-criteria evaluation model. A five-stage model-building process is followed, using a top-down "value-focused thinking" approach, involving literature reviews and expert consultations. A generic value tree is structured capturing decision-makers' concerns for assessing the value of new medicines in the context of Health Technology Assessment (HTA) and in alignment with decision theory. The resulting value tree (Advance Value Tree) consists of three levels of criteria (top level criteria clusters, mid-level criteria, bottom level sub-criteria or attributes) relating to five key domains that can be explicitly measured and assessed: (a) burden of disease, (b) therapeutic impact, (c) safety profile (d) innovation level and (e) socioeconomic impact. A number of MAVT modelling techniques are introduced for operationalising (i.e. estimating) the model, for scoring the alternative treatment options, assigning relative weights of importance to the criteria, and combining scores and weights. Overall, the combination of these MCDA modelling techniques for the elicitation and construction of value preferences across the generic value tree provides a new value framework (Advance Value Framework) enabling the comprehensive measurement of value in a structured and transparent way. Given its flexibility to meet diverse requirements and become readily adaptable across different settings, the Advance Value Framework could be offered as a decision-support tool for evaluators and payers to aid coverage and reimbursement of new medicines. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Learning classification trees

NASA Technical Reports Server (NTRS)

Buntine, Wray

1991-01-01

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

Mapping forest tree species over large areas with partially cloudy Landsat imagery

NASA Astrophysics Data System (ADS)

Turlej, K.; Radeloff, V.

2017-12-01

Forests provide numerous services to natural systems and humankind, but which services forest provide depends greatly on their tree species composition. That makes it important to track not only changes in forest extent, something that remote sensing excels in, but also to map tree species. The main goal of our work was to map tree species with Landsat imagery, and to identify how to maximize mapping accuracy by including partially cloudy imagery. Our study area covered one Landsat footprint (26/28) in Northern Wisconsin, USA, with temperate and boreal forests. We selected this area because it contains numerous tree species and variable forest composition providing an ideal study area to test the limits of Landsat data. We quantified how species-level classification accuracy was affected by a) the number of acquisitions, b) the seasonal distribution of observations, and c) the amount of cloud contamination. We classified a single year stack of Landsat-7, and -8 images data with a decision tree algorithm to generate a map of dominant tree species at the pixel- and stand-level. We obtained three important results. First, we achieved producer's accuracies in the range 70-80% and user's accuracies in range 80-90% for the most abundant tree species in our study area. Second, classification accuracy improved with more acquisitions, when observations were available from all seasons, and is the best when images with up to 40% cloud cover are included. Finally, classifications for pure stands were 10 to 30 percentage points better than those for mixed stands. We conclude that including partially cloudy Landsat imagery allows to map forest tree species with accuracies that were previously only possible for rare years with many cloud-free observations. Our approach thus provides important information for both forest management and science.

Tree species preferences of foraging songbirds during spring migration in floodplain forests of the Upper Mississippi River

USGS Publications Warehouse

Kirsch, Eileen M.; Wellik, Mike J.

2017-01-01

Floodplain forest of the Upper Mississippi River is important for songbirds during spring migration. However, the altered hydrology of this system and spread of reed canary grass (Phalaris arundinacea) and emerald ash borer (Agrilus planipennis) threaten tree diversity and long-term sustainability of this forest. We estimated tree preferences of songbirds during spring migration 2010–2013 to help guide management decisions that promote tree diversity and forest sustainability and to evaluate yearly variation in tree selection. We used the point center-quarter method to assess relative availability of tree species and tallied bird foraging observations on tree species as well as recording the phenophase of used trees on five 40 ha plots of contiguous floodplain forest between La Crosse, Wisconsin and New Albin, Iowa, from 15 April through 1 June. We quantified bird preferences by comparing proportional use of tree species by each bird species to estimates of tree species availability for all 4 y and for each year separately. Species that breed locally preferred silver maple (Acer saccharinum), which is dominant in this forest. The common transient migrant species and the suite of 17 transient wood warbler species preferred hackberry (Celtis occidentalis) and oaks (Quercus spp.), which are limited to higher elevations on the floodplain. We observed earlier leaf development the warm springs of 2010 and 2012 and later leaf development the cold springs of 2011 and 2013. Yellow-rumped Warbler (Setophaga coronata), American Redstart (S. ruticilla), Warbling Vireo (Vireo gilvus) and Baltimore Oriole (Icterus galbula), and the suite of transient migrant wood warblers spread their foraging efforts among tree species in colder springs and were more selective in warmer springs. All three of the important tree species are not regenerating well on the UMR and widespread die-off of silver maple is possible in 50 y without large scale management.

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

PubMed

Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

2016-11-01

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.

PubMed

Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

2012-05-30

The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. Copyright © 2012 Elsevier B.V. All rights reserved.

Three screening methods for cognitive dysfunction using the Mini-Mental State Examination and Korean Dementia Screening Questionnaire.

PubMed

Choi, Seong Hye; Park, Moon Ho

2016-02-01

To screen for and determine cognitive dysfunction, cognitive tests and/or informant reports are commonly used. However, these cognitive tests and informant reports are not always available. The present study investigated three screening methods using the Mini-Mental State Examination (MMSE) as the cognitive test, and the Korean dementia screening questionnaire (KDSQ) as the informant report. Participants were recruited from the Korea Clinical Research Center for Dementia of South Korea, and included 2861 patients with Alzheimer's disease (dementia), 3519 patients with mild cognitive impairment and 1375 controls with no cognitive dysfunction. Three screening methods were tested: (i) MMSE alone (MMSE(cut-off) ); (ii) a conventional combination of MMSE and KDSQ (MMSE+KDSQ(cut-off) ); and (iii) a decision tree with MMSE and KDSQ (MMSE+KDSQ(decision tree) ). For discriminating any cognitive dysfunction from controls, MMSE+KDSQ(cut-off) had the highest area under the receiver operating characteristic curve (0.784). For discriminating dementia from controls, MMSE+KDSQ(cut-off) had the highest area under the receiver operating characteristic curve (0.899). For discriminating mild cognitive impairment from controls, MMSE(cut-off) had the highest area under the receiver operating characteristic curve (0.683). MMSE+KDSQ(decision tree) showed the highest sensitivity for all discriminations. For overall classification accuracy, MMSE+KDSQ(decision tree) had the highest value (70.0%). These three methods had different advantageous properties for screening and staging cognitive dysfunction. As there might be different availability across clinical settings, these three methods can be selected and used according to situational needs. © 2015 Japan Geriatrics Society.

The risk factors of laryngeal pathology in Korean adults using a decision tree model.

PubMed

Byeon, Haewon

2015-01-01

The purpose of this study was to identify risk factors affecting laryngeal pathology in the Korean population and to evaluate the derived prediction model. Cross-sectional study. Data were drawn from the 2008 Korea National Health and Nutritional Examination Survey. The subjects were 3135 persons (1508 male and 2114 female) aged 19 years and older living in the community. The independent variables were age, sex, occupation, smoking, alcohol drinking, and self-reported voice problems. A decision tree analysis was done to identify risk factors for predicting a model of laryngeal pathology. The significant risk factors of laryngeal pathology were age, gender, occupation, smoking, and self-reported voice problem in decision tree model. Four significant paths were identified in the decision tree model for the prediction of laryngeal pathology. Those identified as high risk groups for laryngeal pathology included those who self-reported a voice problem, those who were males in their 50s who did not recognize a voice problem, those who were not economically active males in their 40s, and male workers aged 19 and over and under 50 or 60 and over who currently smoked. The results of this study suggest that individual risk factors, such as age, sex, occupation, health behavior, and self-reported voice problem, affect the onset of laryngeal pathology in a complex manner. Based on the results of this study, early management of the high-risk groups is needed for the prevention of laryngeal pathology. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models

NASA Astrophysics Data System (ADS)

Saha, Debasish; Kemanian, Armen R.; Rau, Benjamin M.; Adler, Paul R.; Montes, Felipe

2017-04-01

Annual cumulative soil nitrous oxide (N2O) emissions calculated from discrete chamber-based flux measurements have unknown uncertainty. We used outputs from simulations obtained with an agroecosystem model to design sampling strategies that yield accurate cumulative N2O flux estimates with a known uncertainty level. Daily soil N2O fluxes were simulated for Ames, IA (corn-soybean rotation), College Station, TX (corn-vetch rotation), Fort Collins, CO (irrigated corn), and Pullman, WA (winter wheat), representing diverse agro-ecoregions of the United States. Fertilization source, rate, and timing were site-specific. These simulated fluxes surrogated daily measurements in the analysis. We ;sampled; the fluxes using a fixed interval (1-32 days) or a rule-based (decision tree-based) sampling method. Two types of decision trees were built: a high-input tree (HI) that included soil inorganic nitrogen (SIN) as a predictor variable, and a low-input tree (LI) that excluded SIN. Other predictor variables were identified with Random Forest. The decision trees were inverted to be used as rules for sampling a representative number of members from each terminal node. The uncertainty of the annual N2O flux estimation increased along with the fixed interval length. A 4- and 8-day fixed sampling interval was required at College Station and Ames, respectively, to yield ±20% accuracy in the flux estimate; a 12-day interval rendered the same accuracy at Fort Collins and Pullman. Both the HI and the LI rule-based methods provided the same accuracy as that of fixed interval method with up to a 60% reduction in sampling events, particularly at locations with greater temporal flux variability. For instance, at Ames, the HI rule-based and the fixed interval methods required 16 and 91 sampling events, respectively, to achieve the same absolute bias of 0.2 kg N ha-1 yr-1 in estimating cumulative N2O flux. These results suggest that using simulation models along with decision trees can reduce the cost and improve the accuracy of the estimations of cumulative N2O fluxes using the discrete chamber-based method.

Inside the black box: starting to uncover the underlying decision rules used in one-by-one expert assessment of occupational exposure in case-control studies

PubMed Central

Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.

2014-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187

Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System.

PubMed

Zhang, Xueyan; Zhang, Jianwu; Li, Lin; Zhang, Yuzhu; Yang, Guocai

2017-02-23

Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern and the same formula rather than considering the orchard terrain features, soil differences, species characteristics and the state of tree growth. With the help of the ZigBee technology, artificial intelligence and decision support technology, this paper has developed the research on the application technology of agricultural Internet of Things for real-time monitoring of citrus soil moisture and nutrients as well as the research on the integration of fertilization and irrigation decision support system. Some achievements were obtained including single-point multi-layer citrus soil temperature and humidity detection wireless sensor nodes and citrus precision fertilization and irrigation management decision support system. They were applied in citrus base in the Three Gorges Reservoir Area. The results showed that the system could help the grower to scientifically fertilize or irrigate, improve the precision operation level of citrus production, reduce the labor cost and reduce the pollution caused by chemical fertilizer.

Team decision problems with classical and quantum signals

PubMed Central

Brandenburger, Adam; La Mura, Pierfrancesco

2016-01-01

We study team decision problems where communication is not possible, but coordination among team members can be realized via signals in a shared environment. We consider a variety of decision problems that differ in what team members know about one another's actions and knowledge. For each type of decision problem, we investigate how different assumptions on the available signals affect team performance. Specifically, we consider the cases of perfectly correlated, i.i.d., and exchangeable classical signals, as well as the case of quantum signals. We find that, whereas in perfect-recall trees (Kuhn 1950 Proc. Natl Acad. Sci. USA 36, 570–576; Kuhn 1953 In Contributions to the theory of games, vol. II (eds H Kuhn, A Tucker), pp. 193–216) no type of signal improves performance, in imperfect-recall trees quantum signals may bring an improvement. Isbell (Isbell 1957 In Contributions to the theory of games, vol. III (eds M Drescher, A Tucker, P Wolfe), pp. 79–96) proved that, in non-Kuhn trees, classical i.i.d. signals may improve performance. We show that further improvement may be possible by use of classical exchangeable or quantum signals. We include an example of the effect of quantum signals in the context of high-frequency trading. PMID:26621985

Spatial modeling and classification of corneal shape.

PubMed

Marsolo, Keith; Twa, Michael; Bullimore, Mark A; Parthasarathy, Srinivasan

2007-03-01

One of the most promising applications of data mining is in biomedical data used in patient diagnosis. Any method of data analysis intended to support the clinical decision-making process should meet several criteria: it should capture clinically relevant features, be computationally feasible, and provide easily interpretable results. In an initial study, we examined the feasibility of using Zernike polynomials to represent biomedical instrument data in conjunction with a decision tree classifier to distinguish between the diseased and non-diseased eyes. Here, we provide a comprehensive follow-up to that work, examining a second representation, pseudo-Zernike polynomials, to determine whether they provide any increase in classification accuracy. We compare the fidelity of both methods using residual root-mean-square (rms) error and evaluate accuracy using several classifiers: neural networks, C4.5 decision trees, Voting Feature Intervals, and Naïve Bayes. We also examine the effect of several meta-learning strategies: boosting, bagging, and Random Forests (RFs). We present results comparing accuracy as it relates to dataset and transformation resolution over a larger, more challenging, multi-class dataset. They show that classification accuracy is similar for both data transformations, but differs by classifier. We find that the Zernike polynomials provide better feature representation than the pseudo-Zernikes and that the decision trees yield the best balance of classification accuracy and interpretability.

Team decision problems with classical and quantum signals.

PubMed

Brandenburger, Adam; La Mura, Pierfrancesco

2016-01-13

We study team decision problems where communication is not possible, but coordination among team members can be realized via signals in a shared environment. We consider a variety of decision problems that differ in what team members know about one another's actions and knowledge. For each type of decision problem, we investigate how different assumptions on the available signals affect team performance. Specifically, we consider the cases of perfectly correlated, i.i.d., and exchangeable classical signals, as well as the case of quantum signals. We find that, whereas in perfect-recall trees (Kuhn 1950 Proc. Natl Acad. Sci. USA 36, 570-576; Kuhn 1953 In Contributions to the theory of games, vol. II (eds H Kuhn, A Tucker), pp. 193-216) no type of signal improves performance, in imperfect-recall trees quantum signals may bring an improvement. Isbell (Isbell 1957 In Contributions to the theory of games, vol. III (eds M Drescher, A Tucker, P Wolfe), pp. 79-96) proved that, in non-Kuhn trees, classical i.i.d. signals may improve performance. We show that further improvement may be possible by use of classical exchangeable or quantum signals. We include an example of the effect of quantum signals in the context of high-frequency trading. © 2015 The Authors.

Diagnostic Features of Common Oral Ulcerative Lesions: An Updated Decision Tree

PubMed Central

Safi, Yaser

2016-01-01

Diagnosis of oral ulcerative lesions might be quite challenging. This narrative review article aims to introduce an updated decision tree for diagnosing oral ulcerative lesions on the basis of their diagnostic features. Various general search engines and specialized databases including PubMed, PubMed Central, Medline Plus, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of MeSH keywords such as “oral ulcer,” “stomatitis,” and “mouth diseases.” Thereafter, English-language articles published since 1983 to 2015 in both medical and dental journals including reviews, meta-analyses, original papers, and case reports were appraised. Upon compilation of the relevant data, oral ulcerative lesions were categorized into three major groups: acute, chronic, and recurrent ulcers and into five subgroups: solitary acute, multiple acute, solitary chronic, multiple chronic, and solitary/multiple recurrent, based on the number and duration of lesions. In total, 29 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by stepwise progression. PMID:27781066

Behaviour change in overweight and obese pregnancy: a decision tree to support the development of antenatal lifestyle interventions.

PubMed

Ainscough, Kate M; Lindsay, Karen L; O'Sullivan, Elizabeth J; Gibney, Eileen R; McAuliffe, Fionnuala M

2017-10-01

Antenatal healthy lifestyle interventions are frequently implemented in overweight and obese pregnancy, yet there is inconsistent reporting of the behaviour-change methods and behavioural outcomes. This limits our understanding of how and why such interventions were successful or not. The current paper discusses the application of behaviour-change theories and techniques within complex lifestyle interventions in overweight and obese pregnancy. The authors propose a decision tree to help guide researchers through intervention design, implementation and evaluation. The implications for adopting behaviour-change theories and techniques, and using appropriate guidance when constructing and evaluating interventions in research and clinical practice are also discussed. To enhance the evidence base for successful behaviour-change interventions during pregnancy, adoption of behaviour-change theories and techniques, and use of published guidelines when designing lifestyle interventions are necessary. The proposed decision tree may be a useful guide for researchers working to develop effective behaviour-change interventions in clinical settings. This guide directs researchers towards key literature sources that will be important in each stage of study development.

Using Boosting Decision Trees in Gravitational Wave Searches triggered by Gamma-ray Bursts

NASA Astrophysics Data System (ADS)

Zuraw, Sarah; LIGO Collaboration

2015-04-01

The search for gravitational wave bursts requires the ability to distinguish weak signals from background detector noise. Gravitational wave bursts are characterized by their transient nature, making them particularly difficult to detect as they are similar to non-Gaussian noise fluctuations in the detector. The Boosted Decision Tree method is a powerful machine learning algorithm which uses Multivariate Analysis techniques to explore high-dimensional data sets in order to distinguish between gravitational wave signal and background detector noise. It does so by training with known noise events and simulated gravitational wave events. The method is tested using waveform models and compared with the performance of the standard gravitational wave burst search pipeline for Gamma-ray Bursts. It is shown that the method is able to effectively distinguish between signal and background events under a variety of conditions and over multiple Gamma-ray Burst events. This example demonstrates the usefulness and robustness of the Boosted Decision Tree and Multivariate Analysis techniques as a detection method for gravitational wave bursts. LIGO, UMass, PREP, NEGAP.

Integrating climate forecasts and natural gas supply information into a natural gas purchasing decision

NASA Astrophysics Data System (ADS)

Changnon, David; Ritsche, Michael; Elyea, Karen; Shelton, Steve; Schramm, Kevin

2000-09-01

This paper illustrates a key lesson related to most uses of long-range climate forecast information, namely that effective weather-related decision-making requires understanding and integration of weather information with other, often complex factors. Northern Illinois University's heating plant manager and staff meteorologist, along with a group of meteorology students, worked together to assess different types of available information that could be used in an autumn natural gas purchasing decision. Weather information assessed included the impact of ENSO events on winters in northern Illinois and the Climate Prediction Center's (CPC) long-range climate outlooks. Non-weather factors, such as the cost and available supplies of natural gas prior to the heating season, contribute to the complexity of the natural gas purchase decision. A decision tree was developed and it incorporated three parts: (a) natural gas supply levels, (b) the CPC long-lead climate outlooks for the region, and (c) an ENSO model developed for DeKalb. The results were used to decide in autumn whether to lock in a price or ride the market each winter. The decision tree was tested for the period 1995-99, and returned a cost-effective decision in three of the four winters.

Moral Fiber?

ERIC Educational Resources Information Center

Zirkel, Perry A.

2000-01-01

Discusses a 2000 federal trial court decision upholding a Kentucky district's termination of a tenured teacher who presented a curricular segment on industrial hemp as part of a "save-the-trees" unit. The decision underscores teachers' severely limited constitutional rights in the curricular context. (MLH)

Assessing the safety of co-exposure to food packaging migrants in food and water using the maximum cumulative ratio and an established decision tree.

PubMed

Price, Paul; Zaleski, Rosemary; Hollnagel, Heli; Ketelslegers, Hans; Han, Xianglu

2014-01-01

Food contact materials can release low levels of multiple chemicals (migrants) into foods and beverages, to which individuals can be exposed through food consumption. This paper investigates the potential for non-carcinogenic effects from exposure to multiple migrants using the Cefic Mixtures Ad hoc Team (MIAT) decision tree. The purpose of the assessment is to demonstrate how the decision tree can be applied to concurrent exposures to multiple migrants using either hazard or structural data on the specific components, i.e. based on the acceptable daily intake (ADI) or the threshold of toxicological concern. The tree was used to assess risks from co-exposure to migrants reported in a study on non-intentionally added substances (NIAS) eluting from food contact-grade plastic and two studies of water bottles: one on organic compounds and the other on ionic forms of various elements. The MIAT decision tree assigns co-exposures to different risk management groups (I, II, IIIA and IIIB) based on the hazard index, and the maximum cumulative ratio (MCR). The predicted co-exposures for all examples fell into Group II (low toxicological concern) and had MCR values of 1.3 and 2.4 (indicating that one or two components drove the majority of the mixture's toxicity). MCR values from the study of inorganic ions (126 mixtures) ranged from 1.1 to 3.8 for glass and from 1.1 to 5.0 for plastic containers. The MCR values indicated that a single compound drove toxicity in 58% of the mixtures. MCR values also declined with increases in the hazard index for the screening assessments of exposure (suggesting fewer substances contributed as risk potential increased). Overall, it can be concluded that the data on co-exposure to migrants evaluated in these case studies are of low toxicological concern and the safety assessment approach described in this paper was shown to be a helpful screening tool.

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran

NASA Astrophysics Data System (ADS)

Zaremotlagh, S.; Hezarkhani, A.

2017-04-01

Some evidences of rare earth elements (REE) concentrations are found in iron oxide-apatite (IOA) deposits which are located in Central Iranian microcontinent. There are many unsolved problems about the origin and metallogenesis of IOA deposits in this district. Although it is considered that felsic magmatism and mineralization were simultaneous in the district, interaction of multi-stage hydrothermal-magmatic processes within the Early Cambrian volcano-sedimentary sequence probably caused some epigenetic mineralizations. Secondary geological processes (e.g., multi-stage mineralization, alteration, and weathering) have affected on variations of major elements and possible redistribution of REE in IOA deposits. Hence, the geochemical behaviors and distribution patterns of REE are expected to be complicated in different zones of these deposits. The aim of this paper is recognizing LREE distribution patterns based on whole-rock chemical compositions and automatic discovery of their geochemical rules. For this purpose, the pattern recognition techniques including decision tree and neural network were applied on a high-dimensional geochemical dataset from Choghart IOA deposit. Because some data features were irrelevant or redundant in recognizing the distribution patterns of each LREE, a greedy attribute subset selection technique was employed to select the best subset of predictors used in classification tasks. The decision trees (CART algorithm) were pruned optimally to more accurately categorize independent test data than unpruned ones. The most effective classification rules were extracted from the pruned tree to describe the meaningful relationships between the predictors and different concentrations of LREE. A feed-forward artificial neural network was also applied to reliably predict the influence of various rock compositions on the spatial distribution patterns of LREE with a better performance than the decision tree induction. The findings of this study could be effectively used to visualize the LREE distribution patterns as geochemical maps.

Analysts guide: TreeVal for Windows, Version 2.0.

Treesearch

R.D. Fight; J.T. Chmelik; E.A. Coulter

2001-01-01

TreeVal for Windows provides financial information and analysis to support silvicultural decisions in coast Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). It integrates the effect of growth and yield, management costs, harvesting costs, product and mill type, manufacturing costs, product prices, and product grade premiums. Output files from...

Naturalistic Decision Making For Power System Operators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greitzer, Frank L.; Podmore, Robin; Robinson, Marck

2009-06-23

Abstract: Motivation -- As indicated by the Blackout of 2003, the North American interconnected electric system is vulnerable to cascading outages and widespread blackouts. Investigations of large scale outages often attribute the causes to the three T’s: Trees, Training and Tools. A systematic approach has been developed to document and understand the mental processes that an expert power system operator uses when making critical decisions. The approach has been developed and refined as part of a capability demonstration of a high-fidelity real-time power system simulator under normal and emergency conditions. To examine naturalistic decision making (NDM) processes, transcripts of operator-to-operatormore » conversations are analyzed to reveal and assess NDM-based performance criteria. Findings/Design -- The results of the study indicate that we can map the Situation Awareness Level of the operators at each point in the scenario. We can also identify clearly what mental models and mental simulations are being performed at different points in the scenario. As a result of this research we expect that we can identify improved training methods and improved analytical and visualization tools for power system operators. Originality/Value -- The research applies for the first time, the concepts of Recognition Primed Decision Making, Situation Awareness Levels and Cognitive Task Analysis to training of electric power system operators. Take away message -- The NDM approach provides an ideal framework for systematic training management and mitigation to accelerate learning in team-based training scenarios with high-fidelity power grid simulators.« less

Effect of altering local protein fluctuations using artificial intelligence

NASA Astrophysics Data System (ADS)

Nishiyama, Katsuhiko

2017-03-01

The fluctuations in Arg111, a significantly fluctuating residue in cathepsin K, were locally regulated by modifying Arg111 to Gly111. The binding properties of 15 dipeptides in the modified protein were analyzed by molecular simulations, and modeled as decision trees using artificial intelligence. The decision tree of the modified protein significantly differed from that of unmodified cathepsin K, and the Arg-to-Gly modification exerted a remarkable effect on the peptide binding properties. By locally regulating the fluctuations of a protein, we may greatly alter the original functions of the protein, enabling novel applications in several fields.

Case-based explanation of non-case-based learning methods.

PubMed Central

Caruana, R.; Kangarloo, H.; Dionisio, J. D.; Sinha, U.; Johnson, D.

1999-01-01

We show how to generate case-based explanations for non-case-based learning methods such as artificial neural nets or decision trees. The method uses the trained model (e.g., the neural net or the decision tree) as a distance metric to determine which cases in the training set are most similar to the case that needs to be explained. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation. PMID:10566351

Introduced and invasive species in novel rangeland ecosystems: friends or foes?

USGS Publications Warehouse

Belnap, Jayne; Ludwig, John A.; Wilcox, Bradford P.; Betancourt, Julio L.; Dean, W. Richard J.; Hoffmann, Benjamin D.; Milton, Sue J.

2012-01-01

Globally, new combinations of introduced and native plant and animal species have changed rangelands into novel ecosystems. Whereas many rangeland stakeholders (people who use or have an interest in rangelands) view intentional species introductions to improve forage and control erosion as beneficial, others focus on unintended costs, such as increased fire risk, loss of rangeland biodiversity, and threats to conservation efforts, specifically in nature reserves and parks. These conflicting views challenge all rangeland stakeholders, especially those making decisions on how best to manage novel ecosystems. To formulate a conceptual framework for decision making, we examined a wide range of novel ecosystems, created by intentional and unintentional introductions of nonnative species and land-use–facilitated spread of native ones. This framework simply divides decision making into two types: 1) straightforward–certain, and 2) complex–uncertain. We argue that management decisions to retain novel ecosystems are certain when goods and services provided by the system far outweigh the costs of restoration, for example in the case of intensively managed Cenchrus pastures. Decisions to return novel ecosystems to natural systems are also certain when the value of the system is low and restoration is easy and inexpensive as in the case of biocontrol of Opuntia infestations. In contrast, decisions whether to retain or restore novel ecosystems become complex and uncertain in cases where benefits are low and costs of control are high as, for example, in the case of stopping the expansion of Prosopis and Juniperus into semiarid rangelands. Decisions to retain or restore novel ecosystems are also complex and uncertain when, for example, nonnative Eucalyptus trees expand along natural streams, negatively affecting biodiversity, but also providing timber and honey. When decision making is complex and uncertain, we suggest that rangeland managers utilize cost–benefit analyses and hold stakeholder workshops to resolve conflicts.

A Multi Criteria Group Decision-Making Model for Teacher Evaluation in Higher Education Based on Cloud Model and Decision Tree

ERIC Educational Resources Information Center

Chang, Ting-Cheng; Wang, Hui

2016-01-01

This paper proposes a cloud multi-criteria group decision-making model for teacher evaluation in higher education which is involving subjectivity, imprecision and fuzziness. First, selecting the appropriate evaluation index depending on the evaluation objectives, indicating a clear structural relationship between the evaluation index and…

The Relation of Student Behavior, Peer Status, Race, and Gender to Decisions about School Discipline Using CHAID Decision Trees and Regression Modeling

ERIC Educational Resources Information Center

Horner, Stacy B.; Fireman, Gary D.; Wang, Eugene W.

2010-01-01

Peer nominations and demographic information were collected from a diverse sample of 1493 elementary school participants to examine behavior (overt and relational aggression, impulsivity, and prosociality), context (peer status), and demographic characteristics (race and gender) as predictors of teacher and administrator decisions about…

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

PubMed

Fraccaro, Paolo; Nicolo, Massimo; Bonetto, Monica; Giacomini, Mauro; Weller, Peter; Traverso, Carlo Enrico; Prosperi, Mattia; OSullivan, Dympna

2015-01-27

To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians' decision pathways to diagnose AMD. Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management.

PubMed

Bellos, Christos; Papadopoulos, Athanassios; Rosso, Roberto; Fotiadis, Dimitrios I

2011-01-01

CHRONIOUS system is an integrated platform aiming at the management of chronic disease patients. One of the most important components of the system is a Decision Support System (DSS) that has been developed in a Smart Device (SD). This component decides on patient's current health status by combining several data, which are acquired either by wearable sensors or manually inputted by the patient or retrieved from the specific database. In case no abnormal situation has been tracked, the DSS takes no action and remains deactivated until next abnormal situation pack of data are being acquired or next scheduled data being transmitted. The DSS that has been implemented is an integrated classification system with two parallel classifiers, combining an expert system (rule-based system) and a supervised classifier, such as Support Vector Machines (SVM), Random Forests, artificial Neural Networks (aNN like the Multi-Layer Perceptron), Decision Trees and Naïve Bayes. The above categorized system is useful for providing critical information about the health status of the patient.

Relative contributions of set-asides and tree retention to the long-term availability of key forest biodiversity structures at the landscape scale.

PubMed

Roberge, Jean-Michel; Lämås, Tomas; Lundmark, Tomas; Ranius, Thomas; Felton, Adam; Nordin, Annika

2015-05-01

Over previous decades new environmental measures have been implemented in forestry. In Fennoscandia, forest management practices were modified to set aside conservation areas and to retain trees at final felling. In this study we simulated the long-term effects of set-aside establishment and tree retention practices on the future availability of large trees and dead wood, two forest structures of documented importance to biodiversity conservation. Using a forest decision support system (Heureka), we projected the amounts of these structures over 200 years in two managed north Swedish landscapes, under management scenarios with and without set-asides and tree retention. In line with common best practice, we simulated set-asides covering 5% of the productive area with priority to older stands, as well as ∼5% green-tree retention (solitary trees and forest patches) including high-stump creation at final felling. We found that only tree retention contributed to substantial increases in the future density of large (DBH ≥35 cm) deciduous trees, while both measures made significant contributions to the availability of large conifers. It took more than half a century to observe stronger increases in the densities of large deciduous trees as an effect of tree retention. The mean landscape-scale volumes of hard dead wood fluctuated widely, but the conservation measures yielded values which were, on average over the entire simulation period, about 2.5 times as high as for scenarios without these measures. While the density of large conifers increased with time in the landscape initially dominated by younger forest, best practice conservation measures did not avert a long-term decrease in large conifer density in the landscape initially comprised of more old forest. Our results highlight the needs to adopt a long temporal perspective and to consider initial landscape conditions when evaluating the large-scale effects of conservation measures on forest biodiversity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Constructing a Graphic Organizer in the Classroom: Introductory Students' Perception of Achievement Using a Decision Map to Solve Aqueous Acid-Base Equilibria Problems

ERIC Educational Resources Information Center

DeMeo, Stephen

2007-01-01

Common examples of graphic organizers include flow diagrams, concept maps, and decision trees. The author has created a novel type of graphic organizer called a decision map. A decision map is a directional heuristic that helps learners solve problems within a generic framework. It incorporates questions that the user must answer and contains…

Exploiting the Free Landsat Archive for Operational Monitoring of Ecosystem Condition and Change Across the Chesapeake Bay Watershed

NASA Technical Reports Server (NTRS)

BrowndeColstoun, Eric

2010-01-01

For the first time, all imagery acquired by the Landsat series of satellites is being made available by the USGS to users at no cost. This represents a key opportunity to use Landsat in a truly operational monitoring framework: large regions of the U.S. such as the Chesapeake Bay Watershed can now be analyzed using "wall-to-wall" imagery at timescales from approximately 1 month to several years. With the future launch of the Landsat Data Continuity Mission (LDCM) and Decadal Survey missions such as the hyperspectral HyspIRI, it is imperative to develop robust processing systems to perform annual ecosystem assessments over large regions such as the Chesapeake Bay. We have been working at NASA's Goddard Space Flight Center (GSFC) to develop an integrative framework for inserting 30m, annual, Landsat based data and derived products into the existing decision support system for the Bay, with a particular focus on ecosystem condition and changes over the entire watershed. The basic goal is to use a 'stack' of Landsat imagery with 40% or less cloud cover to produce multi-date (2005-2009 period), cloud/shadow/gap-free composited surface reflectance products that will support the creation of watershed scale land cover/ use products and the monitoring of ecosystem change across the Bay. Our scientific focus extends beyond the conventional definition of land cover (i.e. a classification of vegetation type) as we propose to monitor both changes in surface type (e.g. forest to urban), vegetation structure (e.g. forest disturbance due to logging or insect damage), as well as winter crop cover. These processes represent a continuum from large, interannual changes in land cover type, to subtler, intra-annual changes associated with short-term disturbance. The free Landsat data are being processed to surface reflectance and composited using the existing Landsat Ecosystem Disturbance Adaptive Processing System here at NASA/ GSFC, and land cover products (type, tree cover, impervious cover, winter cover) are being produced using well-established decision tree and regression tree algorithms. The goal of this session is to present the data products that we have been developing to the Bay science community and to discuss potential avenues for improvements and usage of the products for decision support.

Outsourcing the Portal: Another Branch in the Decision Tree.

ERIC Educational Resources Information Center

McMahon, Tim

2000-01-01

Discussion of the management of information resources in organizations focuses on the use of portal technologies to update intranet capabilities. Considers application outsourcing decisions, reviews benefits (including reducing costs) as well as concerns, and describes application service providers (ASPs). (LRW)

Aid decision algorithms to estimate the risk in congenital heart surgery.

PubMed

Ruiz-Fernández, Daniel; Monsalve Torra, Ana; Soriano-Payá, Antonio; Marín-Alonso, Oscar; Triana Palencia, Eddy

2016-04-01

In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A measuring tool for tree-rings analysis

NASA Astrophysics Data System (ADS)

Shumilov, Oleg; Kanatjev, Alexander; Kasatkina, Elena

2013-04-01

A special tool has been created for the annual tree-ring widths measurement and analysis. It consists of professional scanner, computer system and software. This created complex in many aspects does not yield the similar systems (LINTAB, WinDENDRO), but in comparison to manual measurement systems, it offers a number of advantages: productivity gain, possibility of archiving the results of the measurements at any stage of the processing, operator comfort. It has been developed a new software, allowing processing of samples of different types (cores, saw cuts), including those which is difficult to process, having got a complex wood structure (inhomogeneity of growing in different directions, missed, light and false rings etc.). This software can analyze pictures made with optical scanners, analog or digital cameras. The complex software program was created on programming language C++, being compatible with modern operating systems like Windows X. Annual ring widths are measured along paths traced interactively. These paths can have any orientation and can be created so that ring widths are measured perpendicular to ring boundaries. A graphic of ring-widths in function of the year is displayed on a screen during the analysis and it can be used for visual and numerical cross-dating and comparison with other series or master-chronologies. Ring widths are saved to the text files in a special format, and those files are converted to the format accepted for data conservation in the International Tree-Ring Data Bank. The created complex is universal in application that will allow its use for decision of the different problems in biology and ecology. With help of this complex it has been reconstructed a long-term juniper (1328-2004) and pine (1445-2005) tree-ring chronologies on the base of samples collected at Kola Peninsula (northwestern Russia).

Assessing the Role and Impact of Geospatial Data for Wildland Fire Management Decisions

NASA Astrophysics Data System (ADS)

Klein, E. A.; Lev, S. M.

2016-12-01

The 2015 Wildland and Fire Science and Technology Task Force Final Report, produced by the National Science and Technology Council, Committee on Environment, Natural Resources, and Sustainability, Subcommittee on Disaster Reduction, highlighted the increasing frequency of large wildfires and the growing demand for science to inform critical resource decisions to manage, mitigate, respond to, and recover from wildland fires. Federal spending on fire suppression from 2005-2015 has more than doubled despite policy changes that prioritize the mitigation of fire risks through the use of fuel treatments, prescribed fire, and management of naturally occurring wildfires to protect life and property. Fire suppression policies over the last century have created forests primed for severe fire, and in the face of a changing climate, the benefits of re-introducing fire into once fire-resilient ecosystems are clear. There are a range of complex factors and regional variation associated with wildland fire risk that complicate our understanding and effective management of this hazard. Data derived from Earth-observing (EO) systems and networks are a crucial input for managers when making decisions about fire suppression and fuel management. EO data can also be used to develop pre- and post-fire metrics that can aid in the evaluating the effectiveness of wildland fire management decisions. A value-tree method for mapping the role of EO systems and networks in delivering societal benefit through key Federal objectives related to wildland fire management will be presented. The value-tree methodology utilizes input from subject matter experts to assess the availability and usability of data and data products and to evaluate the impact of individual EO data inputs for achieving wildland fire management objectives. The results provide a qualitative assessment of the value of the data for the objectives described and identify critical gaps and continuity issues associated with improvements to and continuous delivery of societal benefit.

A regret theory approach to decision curve analysis: a novel method for eliciting decision makers' preferences and decision-making.

PubMed

Tsalatsanis, Athanasios; Hozo, Iztok; Vickers, Andrew; Djulbegovic, Benjamin

2010-09-16

Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc).

A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

PubMed Central

2010-01-01

Background Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. Methods First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. Results We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. Conclusions We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc). PMID:20846413

Simulation of California's Major Reservoirs Outflow Using Data Mining Technique

NASA Astrophysics Data System (ADS)

Yang, T.; Gao, X.; Sorooshian, S.

2014-12-01

The reservoir's outflow is controlled by reservoir operators, which is different from the upstream inflow. The outflow is more important than the reservoir's inflow for the downstream water users. In order to simulate the complicated reservoir operation and extract the outflow decision making patterns for California's 12 major reservoirs, we build a data-driven, computer-based ("artificial intelligent") reservoir decision making tool, using decision regression and classification tree approach. This is a well-developed statistical and graphical modeling methodology in the field of data mining. A shuffled cross validation approach is also employed to extract the outflow decision making patterns and rules based on the selected decision variables (inflow amount, precipitation, timing, water type year etc.). To show the accuracy of the model, a verification study is carried out comparing the model-generated outflow decisions ("artificial intelligent" decisions) with that made by reservoir operators (human decisions). The simulation results show that the machine-generated outflow decisions are very similar to the real reservoir operators' decisions. This conclusion is based on statistical evaluations using the Nash-Sutcliffe test. The proposed model is able to detect the most influential variables and their weights when the reservoir operators make an outflow decision. While the proposed approach was firstly applied and tested on California's 12 major reservoirs, the method is universally adaptable to other reservoir systems.

Estimating the influence of different urban canopy cover types on atmospheric particulate matter (PM10) pollution abatement in London UK.

NASA Astrophysics Data System (ADS)

Tallis, Matthew; Freer-Smith, Peter; Sinnett, Danielle; Aylott, Matthew; Taylor, Gail

2010-05-01

In the urban environment atmospheric pollution by PM10 (particulate matter with a diameter less than 10 x 10-6 m) is a problem that can have adverse effects on human health, particularly increasing rates of respiratory disease. The main contributors to atmospheric PM10 in the urban environment are road traffic, industry and power production. The urban tree canopy is a receptor for removing PM10s from the atmosphere due to the large surface areas generated by leaves and air turbulence created by the structure of the urban forest. In this context urban greening has long been known as a mechanism to contribute towards PM10 removal from the air, furthermore, tree canopy cover has a role in contributing towards a more sustainable urban environment. The work reported here has been carried out within the BRIDGE project (SustainaBle uRban plannIng Decision support accountinG for urban mEtabolism). The aim of this project is to assess the fluxes of energy, water, carbon dioxide and particulates within the urban environment and develope a DSS (Decision Support System) to aid urban planners in sustainable development. A combination of published urban canopy cover data from ground, airborne and satellite based surveys was used. For each of the 33 London boroughs the urban canopy was classified to three groups, urban woodland, street trees and garden trees and each group quantified in terms of ground cover. The total [PM10] for each borough was taken from the LAEI (London Atmospheric Emissions Inventory 2006) and the contribution to reducing [PM10] was assessed for each canopy type. Deposition to the urban canopy was assessed using the UFORE (Urban Forest Effects Model) approach. Deposition to the canopy, boundary layer height and percentage reduction of the [PM10] in the atmosphere was assessed using both hourly meterological data and [PM10] and seasonal data derived from annual models. Results from hourly and annual data were compared with measured values. The model was then applied to future predictions of annual [PM10] and future canopy cover scenarios for London. The contribution of each canopy type subjected to the different atmospheric [PM10] of the 33 London boroughs now and in the future will be discussed. Implementing these findings into a decision support system (DSS) for sustainable urban planning will also be discussed.

A novel decision tree approach based on transcranial Doppler sonography to screen for blunt cervical vascular injuries.

PubMed

Purvis, Dianna; Aldaghlas, Tayseer; Trickey, Amber W; Rizzo, Anne; Sikdar, Siddhartha

2013-06-01

Early detection and treatment of blunt cervical vascular injuries prevent adverse neurologic sequelae. Current screening criteria can miss up to 22% of these injuries. The study objective was to investigate bedside transcranial Doppler sonography for detecting blunt cervical vascular injuries in trauma patients using a novel decision tree approach. This prospective pilot study was conducted at a level I trauma center. Patients undergoing computed tomographic angiography for suspected blunt cervical vascular injuries were studied with transcranial Doppler sonography. Extracranial and intracranial vasculatures were examined with a portable power M-mode transcranial Doppler unit. The middle cerebral artery mean flow velocity, pulsatility index, and their asymmetries were used to quantify flow patterns and develop an injury decision tree screening protocol. Student t tests validated associations between injuries and transcranial Doppler predictive measures. We evaluated 27 trauma patients with 13 injuries. Single vertebral artery injuries were most common (38.5%), followed by single internal carotid artery injuries (30%). Compared to patients without injuries, mean flow velocity asymmetry was higher for single internal carotid artery (P = .003) and single vertebral artery (P = .004) injuries. Similarly, pulsatility index asymmetry was higher in single internal carotid artery (P = .015) and single vertebral artery (P = .042) injuries, whereas the lowest pulsatility index was elevated for bilateral vertebral artery injuries (P = .006). The decision tree yielded 92% specificity, 93% sensitivity, and 93% correct classifications. In this pilot feasibility study, transcranial Doppler measures were significantly associated with the blunt cervical vascular injury status, suggesting that transcranial Doppler sonography might be a viable bedside screening tool for trauma. Patient-specific hemodynamic information from transcranial Doppler assessment has the potential to alter patient care pathways to improve outcomes.

Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

PubMed Central

2013-01-01

Background Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. Results A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease. PMID:24188919

Engineering design for pedestrian safety at highway-rail grade crossings

DOT National Transportation Integrated Search

2016-07-01

A number of pedestrian treatments at railroad grade crossings have been developed and are used throughout the United States. The decision of when to use these treatments is generally a matter of best practices, using a decision tree, or conducting a ...

Health and climate related ecosystem services provided by street trees in the urban environment.

PubMed

Salmond, Jennifer A; Tadaki, Marc; Vardoulakis, Sotiris; Arbuthnott, Katherine; Coutts, Andrew; Demuzere, Matthias; Dirks, Kim N; Heaviside, Clare; Lim, Shanon; Macintyre, Helen; McInnes, Rachel N; Wheeler, Benedict W

2016-03-08

Urban tree planting initiatives are being actively promoted as a planning tool to enable urban areas to adapt to and mitigate against climate change, enhance urban sustainability and improve human health and well-being. However, opportunities for creating new areas of green space within cities are often limited and tree planting initiatives may be constrained to kerbside locations. At this scale, the net impact of trees on human health and the local environment is less clear, and generalised approaches for evaluating their impact are not well developed.In this review, we use an urban ecosystems services framework to evaluate the direct, and locally-generated, ecosystems services and disservices provided by street trees. We focus our review on the services of major importance to human health and well-being which include 'climate regulation', 'air quality regulation' and 'aesthetics and cultural services'. These are themes that are commonly used to justify new street tree or street tree retention initiatives. We argue that current scientific understanding of the impact of street trees on human health and the urban environment has been limited by predominantly regional-scale reductionist approaches which consider vegetation generally and/or single out individual services or impacts without considering the wider synergistic impacts of street trees on urban ecosystems. This can lead planners and policymakers towards decision making based on single parameter optimisation strategies which may be problematic when a single intervention offers different outcomes and has multiple effects and potential trade-offs in different places.We suggest that a holistic approach is required to evaluate the services and disservices provided by street trees at different scales. We provide information to guide decision makers and planners in their attempts to evaluate the value of vegetation in their local setting. We show that by ensuring that the specific aim of the intervention, the scale of the desired biophysical effect and an awareness of a range of impacts guide the choice of i) tree species, ii) location and iii) density of tree placement, street trees can be an important tool for urban planners and designers in developing resilient and resourceful cities in an era of climatic change.

Automatic Data Filter Customization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Mandrake, Lukas

2013-01-01

This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.

Visualizing Decision-making Behaviours in Agent-based Autonomous Spacecraft

NASA Technical Reports Server (NTRS)

North, Steve; Hennessy, Joseph F. (Technical Monitor)

2003-01-01

The authors will report initial progress on the PIAudit project as a Research Resident Associate Program. The objective of this research is to prototype a tool for visualizing decision-making behaviours in autonomous spacecraft. This visualization will serve as an information source for human analysts. The current visualization prototype for PIAudit combines traditional Decision Trees with Weights of Evidence.

A parallel decision tree-based method for user authentication based on keystroke patterns.

PubMed

Sheng, Yong; Phoha, Vir V; Rovnyak, Steven M

2005-08-01

We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to complement the 387 vectors of raw data. The training set, including raw and simulated data, is split into four subsets. For each subset, wavelet transforms are performed to obtain a total of eight training subsets for each user. Eight DTs are thus trained using the eight subsets. A parallel DT is constructed for each user, which contains all eight DTs with a criterion for its output that it authenticates the user if at least three DTs do so; otherwise it rejects the user. Training and testing data were collected from 43 users who typed the exact same string of length 37 nine consecutive times to provide data for training purposes. The users typed the same string at various times over a period from November through December 2002 to provide test data. The average false reject rate was 9.62% and the average false accept rate was 0.88%.

Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors.

PubMed

Su, Ruiliang; Chen, Xiang; Cao, Shuai; Zhang, Xu

2016-01-14

Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random forests. This framework was used to recognize Chinese sign language subwords using recordings from a pair of portable devices worn on both arms consisting of accelerometers (ACC) and surface electromyography (sEMG) sensors. The experimental results demonstrated the validity of the proposed random forest-based method for recognition of Chinese sign language (CSL) subwords. With the proposed method, 98.25% average accuracy was obtained for the classification of a list of 121 frequently used CSL subwords. Moreover, the random forests method demonstrated a superior performance in resisting the impact of bad training samples. When the proportion of bad samples in the training set reached 50%, the recognition error rate of the random forest-based method was only 10.67%, while that of a single decision tree adopted in our previous work was almost 27.5%. Our study offers a practical way of realizing a robust and wearable EMG-ACC-based SLR systems.

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

Derivative Trade Optimizing Model Utilizing GP Based on Behavioral Finance Theory

NASA Astrophysics Data System (ADS)

Matsumura, Koki; Kawamoto, Masaru

This paper proposed a new technique which makes the strategy trees for the derivative (option) trading investment decision based on the behavioral finance theory and optimizes it using evolutionary computation, in order to achieve high profitability. The strategy tree uses a technical analysis based on a statistical, experienced technique for the investment decision. The trading model is represented by various technical indexes, and the strategy tree is optimized by the genetic programming(GP) which is one of the evolutionary computations. Moreover, this paper proposed a method using the prospect theory based on the behavioral finance theory to set psychological bias for profit and deficit and attempted to select the appropriate strike price of option for the higher investment efficiency. As a result, this technique produced a good result and found the effectiveness of this trading model by the optimized dealings strategy.

An integrative model for in-silico clinical-genomics discovery science.

PubMed

Lussier, Yves A; Sarkar, Indra Nell; Cantor, Michael

2002-01-01

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

[Study on extraction method of Panax notoginseng plots in Wenshan of Yunnan province based on decision tree model].

PubMed

Shi, Ting-Ting; Zhang, Xiao-Bo; Guo, Lan-Ping; Huang, Lu-Qi

2017-11-01

The herbs used as the material for traditional Chinese medicine are always planted in the mountainous area where the natural environment is suitable. As the mountain terrain is complex and the distribution of planting plots is scattered, the traditional survey method is difficult to obtain accurate planting area. It is of great significance to provide decision support for the conservation and utilization of traditional Chinese medicine resources by studying the method of extraction of Chinese herbal medicine planting area based on remote sensing and realizing the dynamic monitoring and reserve estimation of Chinese herbal medicines. In this paper, taking the Panax notoginseng plots in Wenshan prefecture of Yunnan province as an example, the China-made GF-1multispectral remote sensing images with a 16 m×16 m resolution were obtained. Then, the time series that can reflect the difference of spectrum of P. notoginseng shed and the background objects were selected to the maximum extent, and the decision tree model of extraction the of P. notoginseng plots was constructed according to the spectral characteristics of the surface features. The results showed that the remote sensing classification method based on the decision tree model could extract P. notoginseng plots in the study area effectively. The method can provide technical support for extraction of P. notoginseng plots at county level. Copyright© by the Chinese Pharmaceutical Association.

Fault tree analysis for integrated and probabilistic risk analysis of drinking water systems.

PubMed

Lindhe, Andreas; Rosén, Lars; Norberg, Tommy; Bergstedt, Olof

2009-04-01

Drinking water systems are vulnerable and subject to a wide range of risks. To avoid sub-optimisation of risk-reduction options, risk analyses need to include the entire drinking water system, from source to tap. Such an integrated approach demands tools that are able to model interactions between different events. Fault tree analysis is a risk estimation tool with the ability to model interactions between events. Using fault tree analysis on an integrated level, a probabilistic risk analysis of a large drinking water system in Sweden was carried out. The primary aims of the study were: (1) to develop a method for integrated and probabilistic risk analysis of entire drinking water systems; and (2) to evaluate the applicability of Customer Minutes Lost (CML) as a measure of risk. The analysis included situations where no water is delivered to the consumer (quantity failure) and situations where water is delivered but does not comply with water quality standards (quality failure). Hard data as well as expert judgements were used to estimate probabilities of events and uncertainties in the estimates. The calculations were performed using Monte Carlo simulations. CML is shown to be a useful measure of risks associated with drinking water systems. The method presented provides information on risk levels, probabilities of failure, failure rates and downtimes of the system. This information is available for the entire system as well as its different sub-systems. Furthermore, the method enables comparison of the results with performance targets and acceptable levels of risk. The method thus facilitates integrated risk analysis and consequently helps decision-makers to minimise sub-optimisation of risk-reduction options.

Portable parallel portfolio optimization in the Aurora Financial Management System

NASA Astrophysics Data System (ADS)

Laure, Erwin; Moritsch, Hans

2001-07-01

Financial planning problems are formulated as large scale, stochastic, multiperiod, tree structured optimization problems. An efficient technique for solving this kind of problems is the nested Benders decomposition method. In this paper we present a parallel, portable, asynchronous implementation of this technique. To achieve our portability goals we elected the programming language Java for our implementation and used a high level Java based framework, called OpusJava, for expressing the parallelism potential as well as synchronization constraints. Our implementation is embedded within a modular decision support tool for portfolio and asset liability management, the Aurora Financial Management System.

VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Serrato, M.; Jungho, I.; Jensen, J.

2012-01-17

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using threemore » different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.« less

A study of fuzzy logic ensemble system performance on face recognition problem

NASA Astrophysics Data System (ADS)

Polyakova, A.; Lipinskiy, L.

2017-02-01

Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

Data mining in radiology

PubMed Central

Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining. PMID:25024513

Satellite solar power - Will it pay off

NASA Technical Reports Server (NTRS)

Hazelrigg, G. A., Jr.

1977-01-01

A cost analysis is presented for front-end investments required for the development of a satellite solar power system. The methodology used makes use of risk analysis techniques to quantify the present state of knowledge relevant to the construction and operation of a satellite solar power station 20 years in the future. Results are used to evaluate the 'expected value' of a three-year research program providing additional information which will be used as a basis for a decision to either continue development of the concept at an increasing funding level or to terminate or drastically alter the program. The program is costed phase by phase, and a decision tree is constructed. The estimated probability of success for the research and studies phase is .540. The expected value of a program leading to the construction of 120 systems at a rate of four per year is 12.433 billion dollars.

The Application of Climate Risk Informed Decision Analysis to the Ioland Water Treatment Plant in Lusaka, Zambia

NASA Astrophysics Data System (ADS)

Kucharski, John; Tkach, Mark; Olszewski, Jennifer; Chaudhry, Rabia; Mendoza, Guillermo

2016-04-01

This presentation demonstrates the application of Climate Risk Informed Decision Analysis (CRIDA) at Zambia's principal water treatment facility, The Iolanda Water Treatment Plant. The water treatment plant is prone to unacceptable failures during periods of low hydropower production at the Kafue Gorge Dam Hydroelectric Power Plant. The case study explores approaches of increasing the water treatment plant's ability to deliver acceptable levels of service under the range of current and potential future climate states. The objective of the study is to investigate alternative investments to build system resilience that might have been informed by the CRIDA process, and to evaluate the extra resource requirements by a bilateral donor agency to implement the CRIDA process. The case study begins with an assessment of the water treatment plant's vulnerability to climate change. It does so by following general principals described in "Confronting Climate Uncertainty in Water Resource Planning and Project Design: the Decision Tree Framework". By utilizing relatively simple bootstrapping methods a range of possible future climate states is generated while avoiding the use of more complex and costly downscaling methodologies; that are beyond the budget and technical capacity of many teams. The resulting climate vulnerabilities and uncertainty in the climate states that produce them are analyzed as part of a "Level of Concern" analysis. CRIDA principals are then applied to this Level of Concern analysis in order to arrive at a set of actionable water management decisions. The principal goals of water resource management is to transform variable, uncertain hydrology into dependable services (e.g. water supply, flood risk reduction, ecosystem benefits, hydropower production, etc…). Traditional approaches to climate adaptation require the generation of predicted future climate states but do little guide decision makers how this information should impact decision making. In this context it is not surprising that the increased hydrologic variability and uncertainty produced by many climate risk analyses bedevil water resource decision making. The Climate Risk Informed Decision Analysis (CRIDA) approach builds on work found in "Confronting Climate Uncertainty in Water Resource Planning and Project Design: the Decision Tree Framework" which provide guidance of vulnerability assessments. It guides practitioners through a "Level of Concern" analysis where climate vulnerabilities are analyzed to produce actionable alternatives and decisions.

Climate-diameter growth relationships of black spruce and jack pine trees in boreal Ontario, Canada.

PubMed

Subedi, Nirmal; Sharma, Mahadev

2013-02-01

To predict the long-term effects of climate change - global warming and changes in precipitation - on the diameter (radial) growth of jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana [Mill.] B.S.P.) trees in boreal Ontario, we modified an existing diameter growth model to include climate variables. Diameter chronologies of 927 jack pine and 1173 black spruce trees, growing in the area from 47°N to 50°N and 80°W to 92°W, were used to develop diameter growth models in a nonlinear mixed-effects approach. Our results showed that the variables long-term average of mean growing season temperature, precipitation during wettest quarter, and total precipitation during growing season were significant (alpha = 0.05) in explaining variation in diameter growth of the sample trees. Model results indicated that higher temperatures during the growing season would increase the diameter growth of jack pine trees, but decrease that of black spruce trees. More precipitation during the wettest quarter would favor the diameter growth of both species. On the other hand, a wetter growing season, which may decrease radiation inputs, increase nutrient leaching, and reduce the decomposition rate, would reduce the diameter growth of both species. Moreover, our results indicated that future (2041-2070) diameter growth rate may differ from current (1971-2000) growth rates for both species, with conditions being more favorable for jack pine than black spruce trees. Expected future changes in the growth rate of boreal trees need to be considered in forest management decisions. We recommend that knowledge of climate-growth relationships, as represented by models, be combined with learning from adaptive management to reduce the risks and uncertainties associated with forest management decisions. © 2012 Blackwell Publishing Ltd.

Asphalt concrete overlays on CRCP : decision criteria, tack coat evaluation, and asphalt concrete mixture evaluation.

DOT National Transportation Integrated Search

2005-02-01

This report presents the research undertaken within two areas of study of thin asphalt concrete (AC) overlays to rehabilitate : continuously reinforced concrete pavements (CRCP). The first one is the development of a decision tree for the project : s...

[Human body meridian spatial decision support system for clinical treatment and teaching of acupuncture and moxibustion].

PubMed

Wu, Dehua

2016-01-01

The spatial position and distribution of human body meridian are expressed limitedly in the decision support system (DSS) of acupuncture and moxibustion at present, which leads to the failure to give the effective quantitative analysis on the spatial range and the difficulty for the decision-maker to provide a realistic spatial decision environment. Focusing on the limit spatial expression in DSS of acupuncture and moxibustion, it was proposed that on the basis of the geographic information system, in association of DSS technology, the design idea was developed on the human body meridian spatial DSS. With the 4-layer service-oriented architecture adopted, the data center integrated development platform was taken as the system development environment. The hierarchical organization was done for the spatial data of human body meridian via the directory tree. The structured query language (SQL) server was used to achieve the unified management of spatial data and attribute data. The technologies of architecture, configuration and plug-in development model were integrated to achieve the data inquiry, buffer analysis and program evaluation of the human body meridian spatial DSS. The research results show that the human body meridian spatial DSS could reflect realistically the spatial characteristics of the spatial position and distribution of human body meridian and met the constantly changeable demand of users. It has the powerful spatial analysis function and assists with the scientific decision in clinical treatment and teaching of acupuncture and moxibustion. It is the new attempt to the informatization research of human body meridian.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree.

PubMed

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen-host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

hERG blocking potential of acids and zwitterions characterized by three thresholds for acidity, size and reactivity.

PubMed

Nikolov, Nikolai G; Dybdahl, Marianne; Jónsdóttir, Svava Ó; Wedebye, Eva B

2014-11-01

Ionization is a key factor in hERG K(+) channel blocking, and acids and zwitterions are known to be less probable hERG blockers than bases and neutral compounds. However, a considerable number of acidic compounds block hERG, and the physico-chemical attributes which discriminate acidic blockers from acidic non-blockers have not been fully elucidated. We propose a rule for prediction of hERG blocking by acids and zwitterionic ampholytes based on thresholds for only three descriptors related to acidity, size and reactivity. The training set of 153 acids and zwitterionic ampholytes was predicted with a concordance of 91% by a decision tree based on the rule. Two external validations were performed with sets of 35 and 48 observations, respectively, both showing concordances of 91%. In addition, a global QSAR model of hERG blocking was constructed based on a large diverse training set of 1374 chemicals covering all ionization classes, externally validated showing high predictivity and compared to the decision tree. The decision tree was found to be superior for the acids and zwitterionic ampholytes classes. Copyright © 2014 Elsevier Ltd. All rights reserved.

A practical guidance for Cramer class determination.

PubMed

Roberts, David W; Aptula, Aynur; Schultz, Terry W; Shen, Jie; Api, Anne Marie; Bhatia, Sneha; Kromidas, Lambros

2015-12-01

Expanded use of the Threshold of Toxicological Concern (TTC) methodology has brought into discussion the intent of the original questions used in the Cramer scheme or Cramer decision tree. We have analysed, both manually and by Toxtree software, a large dataset of fragrance ingredients and identified several issues with the original Cramer questions. Some relate to definitions and wording of questions; others relate to in silico interpretation of the questions. We have endeavoured to address all of these inconsistencies and misinterpretations without changing the basic structure and principles of the original decision tree. Based on the analysis of a large data set of over 2500 fragrance ingredients, we found that most of the 33 questions in the original Cramer scheme are straightforward. Through repeated examination each of the 33 questions, we found 14 where the logic underlying the development of the rule is unclear. These questions are well served by minor wording changes and/or further explanation designed to capture what we perceive to be the intent of the original decision tree. The findings reported here could be used as a guidance for conducting Cramer classification and provide advices for the improvement of the in silico tools. Copyright © 2015 Elsevier Inc. All rights reserved.

Peripheral Exophytic Oral Lesions: A Clinical Decision Tree

PubMed Central

Safi, Yaser; Jafari, Soudeh

2017-01-01

Diagnosis of peripheral oral exophytic lesions might be quite challenging. This review article aimed to introduce a decision tree for oral exophytic lesions according to their clinical features. General search engines and specialized databases including PubMed, PubMed Central, Medline Plus, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of keywords such as “oral soft tissue lesion,” “oral tumor like lesion,” “oral mucosal enlargement,” and “oral exophytic lesion.” Related English-language articles published since 1988 to 2016 in both medical and dental journals were appraised. Upon compilation of data, peripheral oral exophytic lesions were categorized into two major groups according to their surface texture: smooth (mesenchymal or nonsquamous epithelium-originated) and rough (squamous epithelium-originated). Lesions with smooth surface were also categorized into three subgroups according to their general frequency: reactive hyperplastic lesions/inflammatory hyperplasia, salivary gland lesions (nonneoplastic and neoplastic), and mesenchymal lesions (benign and malignant neoplasms). In addition, lesions with rough surface were summarized in six more common lesions. In total, 29 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by a stepwise progression method. PMID:28757870

Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Lin, Chuan-Yao; Liau, Churn-Jung; Kuo, Yi-Ming

2012-12-01

Kaohsiung City and the suburban region of southwestern Taiwan have suffered from severe air pollution since becoming the largest center of heavy industry in Taiwan. The complex process of ozone (O3) formation and its precursor compounds (the volatile organic compounds (VOCs) and nitrogen oxide (NOx) emissions), accompanied by meteorological conditions, make controlling ozone difficult. Using a decision tree is especially appropriate for analyzing time series data that contain ozone levels and meteorological and explanatory variables for ozone formation. Results show that dominant variables such as temperature, wind speed, VOCs, and NOx can play vital roles in describing ozone variations among observations. That temperature and wind speed are highly correlated with ozone levels indicates that these meteorological conditions largely affect ozone variability. The results also demonstrate that spatial heterogeneity of ozone patterns are in coastal and inland areas caused by sea-land breeze and pollutant sources during high ozone episodes over southwestern Taiwan. This study used a decision tree to obtain quantitative insight into spatial distributions of precursor compound emissions and effects of meteorological conditions on ozone levels that are useful for refining monitoring plans and developing management strategies.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree

PubMed Central

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules. PMID:26649272

A structured approach to Exposure Based Waiving of human health endpoints under REACH developed in the OSIRIS project.

PubMed

Marquart, Hans; Meijster, Tim; Van de Bovenkamp, Marja; Ter Burg, Wouter; Spaan, Suzanne; Van Engelen, Jacqueline

2012-03-01

Exposure Based Waiving (EBW) is one of the options in REACH when there is insufficient hazard data on a specific endpoint. Rules for adaptation of test requirements are specified and a general option for EBW is given via Appendix XI of REACH, allowing waiving of repeated dose toxicity studies, reproductive toxicity studies and carcinogenicity studies under a number of conditions if exposure is very low. A decision tree is described that was developed in the European project OSIRIS (Optimised Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-Test and Test Information) to help decide in what cases EBW can be justified. The decision tree uses specific criteria as well as more general questions. For the latter, guidance on interpretation and resulting conclusions is provided. Criteria and guidance are partly based on an expert elicitation process. Among the specific criteria a number of proposed Thresholds of Toxicological Concern are used. The decision tree, expanded with specific parts on absorption, distribution, metabolism and excretion that are not described in this paper, is implemented in the OSIRIS webtool on integrated testing strategies. Copyright © 2011 Elsevier Inc. All rights reserved.

An integrated approach to system design, reliability, and diagnosis

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Iverson, David L.

1990-01-01

The requirement for ultradependability of computer systems in future avionics and space applications necessitates a top-down, integrated systems engineering approach for design, implementation, testing, and operation. The functional analyses of hardware and software systems must be combined by models that are flexible enough to represent their interactions and behavior. The information contained in these models must be accessible throughout all phases of the system life cycle in order to maintain consistency and accuracy in design and operational decisions. One approach being taken by researchers at Ames Research Center is the creation of an object-oriented environment that integrates information about system components required in the reliability evaluation with behavioral information useful for diagnostic algorithms. Procedures have been developed at Ames that perform reliability evaluations during design and failure diagnoses during system operation. These procedures utilize information from a central source, structured as object-oriented fault trees. Fault trees were selected because they are a flexible model widely used in aerospace applications and because they give a concise, structured representation of system behavior. The utility of this integrated environment for aerospace applications in light of our experiences during its development and use is described. The techniques for reliability evaluation and failure diagnosis are discussed, and current extensions of the environment and areas requiring further development are summarized.

Operational Performance Risk Assessment in Support of A Supervisory Control System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Denning, Richard S.; Muhlheim, Michael David; Cetiner, Sacit M.

Supervisory control system (SCS) is developed for multi-unit advanced small modular reactors to minimize human interventions in both normal and abnormal operations. In SCS, control action decisions made based on probabilistic risk assessment approach via Event Trees/Fault Trees. Although traditional PRA tools are implemented, their scope is extended to normal operations and application is reversed; success of non-safety related system instead failure of safety systems this extended PRA approach called as operational performance risk assessment (OPRA). OPRA helps to identify success paths, combination of control actions for transients and to quantify these success paths to provide possible actions without activatingmore » plant protection system. In this paper, a case study of the OPRA in supervisory control system is demonstrated within the context of the ALMR PRISM design, specifically power conversion system. The scenario investigated involved a condition that the feed water control valve is observed to be drifting to the closed position. Alternative plant configurations were identified via OPRA that would allow the plant to continue to operate at full or reduced power. Dynamic analyses were performed with a thermal-hydraulic model of the ALMR PRISM system using Modelica to evaluate remained safety margins. Successful recovery paths for the selected scenario are identified and quantified via SCS.« less

An integrated approach to system design, reliability, and diagnosis

NASA Astrophysics Data System (ADS)

Patterson-Hine, F. A.; Iverson, David L.

1990-12-01

The requirement for ultradependability of computer systems in future avionics and space applications necessitates a top-down, integrated systems engineering approach for design, implementation, testing, and operation. The functional analyses of hardware and software systems must be combined by models that are flexible enough to represent their interactions and behavior. The information contained in these models must be accessible throughout all phases of the system life cycle in order to maintain consistency and accuracy in design and operational decisions. One approach being taken by researchers at Ames Research Center is the creation of an object-oriented environment that integrates information about system components required in the reliability evaluation with behavioral information useful for diagnostic algorithms. Procedures have been developed at Ames that perform reliability evaluations during design and failure diagnoses during system operation. These procedures utilize information from a central source, structured as object-oriented fault trees. Fault trees were selected because they are a flexible model widely used in aerospace applications and because they give a concise, structured representation of system behavior. The utility of this integrated environment for aerospace applications in light of our experiences during its development and use is described. The techniques for reliability evaluation and failure diagnosis are discussed, and current extensions of the environment and areas requiring further development are summarized.

Evaluating the Effectiveness of Science for Decision-Making: Water Managers and Tree- Ring Data in the Western United States

NASA Astrophysics Data System (ADS)

Rice, J. L.; Woodhouse, C.; Lukas, J.

2008-12-01

Current climate variability, potential impacts of climate change, and limited resources in the face of growing demand are increasingly prompting water managers in the western United States to consider and use data from climate-related research in water resource planning. Much of these data are produced by stakeholder- driven science programs, such as NOAA's Regional Integrated Science Assessments (RISAs), but there have been few efforts to evaluate the effectiveness of these science-to-application efforts. Over the past several years, researchers with the Western Water Assessment (WWA) RISA have been providing tree-ring reconstructions of streamflow to water managers in Colorado and other western states, and presenting technical workshops explaining the applications of these tree-ring data for water management and planning. Using in-depth interviews and a survey questionnaire, we have assessed the effectiveness and outcomes of these engagements, addressing (1) the factors that have prompted water managers to seek out tree-ring data, (2) how paleoclimate data has been made relevant and accessible for water resource planning, and (3) how tree-ring data and information have been utilized by water managers and other workshop participants. We also provide an assessment of challenges and opportunities that exist in the translation of climate science for decision-making, including how tree-ring data are interpreted in the context of water planning paradigms, issues of credibility and acceptance of tree ring data, and what data needs exist in different planning environments. These findings have broader application in improving and evaluating science-policy interactions related to climate and climate change.

Prediction of Disease Case Severity Level To Determine INA CBGs Rate

NASA Astrophysics Data System (ADS)

Puspitorini, Sukma; Kusumadewi, Sri; Rosita, Linda

2017-03-01

Indonesian Case-Based Groups (INA CBGs) is case-mix payment system using software grouper application. INA CBGs consisting of four digits code where the last digits indicating the severity level of disease cases. Severity level influence by secondary diagnosis (complications and co-morbidity) related to resource intensity level. It is medical resources used to treat a hospitalized patient. Objectives of this research is developing decision support system to predict severity level of disease cases and illustrate INA CBGs rate by using data mining decision tree classification model. Primary diagnosis (DU), first secondary diagnosis (DS 1), and second secondary diagnosis (DS 2) are attributes that used as input of severity level. The training process using C4.5 algorithm and the rules will represent in the IF-THEN form. Credibility of the system analyzed through testing process and confusion matrix present the results. Outcome of this research shows that first secondary diagnosis influence significant to form severity level predicting rules from new disease cases and INA CBGs rate illustration.

i-Tree: Tools to assess and manage structure, function, and value of community forests

NASA Astrophysics Data System (ADS)

Hirabayashi, S.; Nowak, D.; Endreny, T. A.; Kroll, C.; Maco, S.

2011-12-01

Trees in urban communities can mitigate many adverse effects associated with anthropogenic activities and climate change (e.g. urban heat island, greenhouse gas, air pollution, and floods). To protect environmental and human health, managers need to make informed decisions regarding urban forest management practices. Here we present the i-Tree suite of software tools (www.itreetools.org) developed by the USDA Forest Service and their cooperators. This software suite can help urban forest managers assess and manage the structure, function, and value of urban tree populations regardless of community size or technical capacity. i-Tree is a state-of-the-art, peer-reviewed Windows GUI- or Web-based software that is freely available, supported, and continuously refined by the USDA Forest Service and their cooperators. Two major features of i-Tree are 1) to analyze current canopy structures and identify potential planting spots, and 2) to estimate the environmental benefits provided by the trees, such as carbon storage and sequestration, energy conservation, air pollution removal, and storm water reduction. To cover diverse forest topologies, various tools were developed within the i-Tree suite: i-Tree Design for points (individual trees), i-Tree Streets for lines (street trees), and i-Tree Eco, Vue, and Canopy (in the order of complexity) for areas (community trees). Once the forest structure is identified with these tools, ecosystem services provided by trees can be estimated with common models and protocols, and reports in the form of texts, charts, and figures are then created for users. Since i-Tree was developed with a client/server architecture, nationwide data in the US such as location-related parameters, weather, streamflow, and air pollution data are stored in the server and retrieved to a user's computer at run-time. Freely available remote-sensed images (e.g. NLCD and Google maps) are also employed to estimate tree canopy characteristics. As the demand for i-Tree grows internationally, environmental databases from more countries will be coupled with the software suite. Two more i-Tree applications, i-Tree Forecast and i-Tree Landscape are now under development. i-Tree Forecast simulates canopy structures for up to 100 years based on planting and mortality rates and adds capabilities for other i-Tree applications to estimate the benefits of future canopy scenarios. While most i-Tree applications employ a spatially lumped approach, i-Tree landscape employs a spatially distributed approach that allows users to map changes in canopy cover and ecosystem services through time and space. These new i-Tree tools provide an advanced platform for urban managers to assess the impact of current and future urban forests. i-Tree allows managers to promote effective urban forest management and sound arboricultural practices by providing information for advocacy and planning, baseline data for making informed decisions, and standardization for comparisons with other communities.