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,…

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.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. R.; Landgrebe, David

1991-01-01

Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

Building of fuzzy decision trees using ID3 algorithm

NASA Astrophysics Data System (ADS)

Begenova, S. B.; Avdeenko, T. V.

2018-05-01

Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.

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.

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.

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.

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.

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.

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.

Attitudes and decision-making about early-infant versus early-adolescent male circumcision: Demand-side insights for sustainable HIV prevention strategies in Zambia and Zimbabwe.

PubMed

Sgaier, Sema K; Sharma, Sunny; Eletskaya, Maria; Prasad, Ram; Mugurungi, Owen; Tambatamba, Bushimbwa; Ncube, Getrude; Xaba, Sinokuthemba; Nanga, Alice; Gumede-Moyo, Sehlulekile; Kretschmer, Steve

2017-01-01

As countries approach their scale-up targets for the voluntary medical male circumcision program for HIV prevention, they are strategizing and planning for the sustainability phase to follow. Global guidance recommends circumcising adolescent (below 14 years) and/or early infant boys (aged 0-60 days), and countries need to consider several factors before prioritizing a cohort for their sustainability phase. We provide community and healthcare provider-side insights on attitudes and decision-making process as a key input for this strategic decision in Zambia and Zimbabwe. We studied expectant parents, parents of infant boys (aged 0-60 days), family members and neo-natal and ante-natal healthcare providers in Zambia and Zimbabwe. Our integrated methodology consisted of in-depth qualitative and quantitative one-on-one interviews, and a simulated-decision-making game, to uncover attitudes towards, and the decision-making process for, early adolescent or early infant medical circumcision (EAMC or EIMC). In both countries, parents viewed early infancy and early adolescence as equally ideal ages for circumcision (38% EIMC vs. 37% EAMC in Zambia; 24% vs. 27% in Zimbabwe). If offered for free, about half of Zambian parents and almost 2 in 5 Zimbabwean parents indicated they would likely circumcise their infant boy; however, half of parents in each country perceived that the community would not accept EIMC. Nurses believed their facilities currently could not absorb EIMC services and that they would have limited ability to influence fathers, who were seen as having the primary decision-making authority. Our analysis suggests that EAMC is more accepted by the community than EIMC and is the path of least resistance for the sustainability phase of VMMC. However, parents or community members do not reject EIMC. Should countries choose to prioritize this cohort for their sustainability phase, a number of barriers around information, decision-making by parents, and supply side

Attitudes and decision-making about early-infant versus early-adolescent male circumcision: Demand-side insights for sustainable HIV prevention strategies in Zambia and Zimbabwe

PubMed Central

Sgaier, Sema K.; Sharma, Sunny; Eletskaya, Maria; Prasad, Ram; Mugurungi, Owen; Tambatamba, Bushimbwa; Ncube, Getrude; Xaba, Sinokuthemba; Nanga, Alice; Gumede-Moyo, Sehlulekile; Kretschmer, Steve

2017-01-01

As countries approach their scale-up targets for the voluntary medical male circumcision program for HIV prevention, they are strategizing and planning for the sustainability phase to follow. Global guidance recommends circumcising adolescent (below 14 years) and/or early infant boys (aged 0–60 days), and countries need to consider several factors before prioritizing a cohort for their sustainability phase. We provide community and healthcare provider-side insights on attitudes and decision-making process as a key input for this strategic decision in Zambia and Zimbabwe. We studied expectant parents, parents of infant boys (aged 0–60 days), family members and neo-natal and ante-natal healthcare providers in Zambia and Zimbabwe. Our integrated methodology consisted of in-depth qualitative and quantitative one-on-one interviews, and a simulated-decision-making game, to uncover attitudes towards, and the decision-making process for, early adolescent or early infant medical circumcision (EAMC or EIMC). In both countries, parents viewed early infancy and early adolescence as equally ideal ages for circumcision (38% EIMC vs. 37% EAMC in Zambia; 24% vs. 27% in Zimbabwe). If offered for free, about half of Zambian parents and almost 2 in 5 Zimbabwean parents indicated they would likely circumcise their infant boy; however, half of parents in each country perceived that the community would not accept EIMC. Nurses believed their facilities currently could not absorb EIMC services and that they would have limited ability to influence fathers, who were seen as having the primary decision-making authority. Our analysis suggests that EAMC is more accepted by the community than EIMC and is the path of least resistance for the sustainability phase of VMMC. However, parents or community members do not reject EIMC. Should countries choose to prioritize this cohort for their sustainability phase, a number of barriers around information, decision-making by parents, and supply side

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.

The effect of joint contraceptive decisions on the use of Injectables, Long-Acting and Permanent Methods (ILAPMs) among married female (15-49) contraceptive users in Zambia: a cross-sectional study.

PubMed

Mutombo, Namuunda; Bakibinga, Pauline

2014-07-03

Zambia's fertility rate and unmet need for family planning are still high. This is in spite of the progress reported from 1992 to 2007 of the increase in contraceptive prevalence rate from 15% to 41% and use of modern methods of family planning from 9% to 33%. However, partner disapproval of family planning has been cited by many women in many countries including Zambia. Given the effectiveness of long-acting and permanent methods of family planning (ILAPMs) in fertility regulation, this paper sought to examine the relationship between contraceptive decision-making and use of ILAPMs among married women in Zambia. This paper uses data from the 2007 Zambia Demographic and Health Survey. The analysis is based on married women (15-49) who reported using a method of family planning at the time of the survey. Out of the 7,146 women interviewed, only 1,630 women were valid for this analysis. Cross-tabulations and binary logistic regressions with Chi-square were used to analyse associations and the predictors of use of ILAPMs of contraception, respectively. A confidence interval of .95 was used in determining relationships between independent and dependent variables. Two thirds of women made joint decisions regarding contraception and 29% of the women were using ILAPMs. Women who made joint contraceptive decisions are significantly more likely to use ILAPMs than women who did not involve their husband in contraceptive decisions. However, the most significant predictor is the wealth index. Women from rich households are more likely to use ILAPMs than women from medium rich and poor households. Results also show that women of North Western ethnicities and those from Region 3 had higher odds of using ILAPMs than Tonga women and women from Region 2, respectively. Joint contraceptive decision-making between spouses is key to use of ILAPMs in Zambia. Our findings have also shown that the wealth index is actually the strongest factor determining use of these methods. As such

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.

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 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] ...

Computerized Adaptive Test vs. decision trees: Development of a support decision system to identify suicidal behavior.

PubMed

Delgado-Gomez, D; Baca-Garcia, E; Aguado, D; Courtet, P; Lopez-Castroman, J

2016-12-01

Several Computerized Adaptive Tests (CATs) have been proposed to facilitate assessments in mental health. These tests are built in a standard way, disregarding useful and usually available information not included in the assessment scales that could increase the precision and utility of CATs, such as the history of suicide attempts. Using the items of a previously developed scale for suicidal risk, we compared the performance of a standard CAT and a decision tree in a support decision system to identify suicidal behavior. We included the history of past suicide attempts as a class for the separation of patients in the decision tree. The decision tree needed an average of four items to achieve a similar accuracy than a standard CAT with nine items. The accuracy of the decision tree, obtained after 25 cross-validations, was 81.4%. A shortened test adapted for the separation of suicidal and non-suicidal patients was developed. CATs can be very useful tools for the assessment of suicidal risk. However, standard CATs do not use all the information that is available. A decision tree can improve the precision of the assessment since they are constructed using a priori information. Copyright © 2016 Elsevier B.V. All rights reserved.

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

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.

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.

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

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.

Zambia.

PubMed

1988-08-01

Attention in this discussion of Zambia is directed to the following: geography; the people; history; government; the economy; foreign relations; defense; and relations between Zambia and the US. In 1986, the population totaled 7 million with an annual growth rate of 3.7%. The infant mortality rate is 87/1000 with a life expectancy of 51 years. Zambia, located in south-central Africa, is bordered by Zaire, Tanzania, Malawi, Mozambique, Zimbabwe, Botswana, Angola, and Namibia. The population is made up of over 70 Bantu-speaking tribes. Expatriates, mostly British (15,000 in 1986) or South African, live primarily in Lusaka where they are employed in mines and related activities. Some ancestors of present-day Zambians most likely arrived about 2000 years ago and eventually displaced or absorbed indigenous stone age hunters and gatherers. The major waves of Bantu-speaking immigrants began in the 15th century; the greatest influx occurred in the late 17th to the early 19th centuries. After the mid-19th century, the area was penetrated by Western explorers. In 1888, Northern and Southern Rhodesia (now Zambia and Zimbabwe) were proclaimed a British sphere of influence. Southern Rhodesia was annexed formally and granted self-government in 1923. Independence was realized on October 24, 1964. Zambia was the 1st British territory to become a republic immediately upon realizing independence. The constitution promulgated on August 25, 1973, abrogated the original 1964 constitution, and this new constitution and the national elections that followed in December 1973 were the final steps in achieving what is termed a "1-party participatory democracy." President Kenneth Kaunda is the major figure in the country's politics. He has wide popular support and traditionally has bridged the rivalries among the country's various regions and ethnic groups. The economy of Zambia is based primarily on its majority state-owned copper industry, which is the only significant source of foreign

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.

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.

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.

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.

Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

DOEpatents

Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

2006-06-13

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

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.

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

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.

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...

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.

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.

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

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)

A universal hybrid decision tree classifier design for human activity classification.

PubMed

Chien, Chieh; Pottie, Gregory J

2012-01-01

A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. The system was tested using seven subjects each monitored by 14 triaxial accelerometers. Each subject performed fourteen different activities typical of daily life. Using leave-one-out cross validation, our decision tree produced average classification accuracies of 89.9%. In contrast, the MATLAB personalized tree classifiers using Gini's diversity index as the split criterion followed by optimally tuning the thresholds for each subject yielded 69.2%.

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+.

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

The effect of joint contraceptive decisions on the use of Injectables, Long-Acting and Permanent Methods (ILAPMs) among married female (15–49) contraceptive users in Zambia: a cross-sectional study

PubMed Central

2014-01-01

Background Zambia’s fertility rate and unmet need for family planning are still high. This is in spite of the progress reported from 1992 to 2007 of the increase in contraceptive prevalence rate from 15% to 41% and use of modern methods of family planning from 9% to 33%. However, partner disapproval of family planning has been cited by many women in many countries including Zambia. Given the effectiveness of long-acting and permanent methods of family planning (ILAPMs) in fertility regulation, this paper sought to examine the relationship between contraceptive decision-making and use of ILAPMs among married women in Zambia. Methods This paper uses data from the 2007 Zambia Demographic and Health Survey. The analysis is based on married women (15–49) who reported using a method of family planning at the time of the survey. Out of the 7,146 women interviewed, only 1,630 women were valid for this analysis. Cross-tabulations and binary logistic regressions with Chi-square were used to analyse associations and the predictors of use of ILAPMs of contraception, respectively. A confidence interval of .95 was used in determining relationships between independent and dependent variables. Results Two thirds of women made joint decisions regarding contraception and 29% of the women were using ILAPMs. Women who made joint contraceptive decisions are significantly more likely to use ILAPMs than women who did not involve their husband in contraceptive decisions. However, the most significant predictor is the wealth index. Women from rich households are more likely to use ILAPMs than women from medium rich and poor households. Results also show that women of North Western ethnicities and those from Region 3 had higher odds of using ILAPMs than Tonga women and women from Region 2, respectively. Conclusion Joint contraceptive decision-making between spouses is key to use of ILAPMs in Zambia. Our findings have also shown that the wealth index is actually the strongest factor

Using histograms to introduce randomization in the generation of ensembles of decision trees

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

2005-02-22

A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

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)

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.

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.

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.

Learning from examples - Generation and evaluation of decision trees for software resource analysis

NASA Technical Reports Server (NTRS)

Selby, Richard W.; Porter, Adam A.

1988-01-01

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

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.

The Republic of Zambia.

PubMed

Hakkert, R; Wieringa, R

1986-05-01

In 1964, at independence, Zambia's economic future looked brighter than that of most other developing countries. Its copper production accounted for 8% of total world production, and only neighboring Zaire outpaced it in the production of cobalt. Its Central Province around Kabwe held rich deposits of both zinc and lead; uranium deposits also had been found, but their projected yield remained undetermined. Since 1974, the decline in the price of copper and the increase in the price of oil have played havoc with Zambia's balance of payments. Copper, which accounted for 40% of the gross national product (GNP) and 98% of all foreign exchange in 1964, shrank to 12% of the GNP in 1978 while still generating most of the foreign exchange. As a result, imports were cut back markedly from $1.5 billion in 1973 to $690 million in 1983. Although this trend is beginning to make a U-turn, Zambia's economic situation is grave. In 1984 the GNP continued to register negative growth and inflation stood at 25%. With its urbanization rate doubling from 21% in 1964 to 43% in 1985, Zambia is now the most urbanized country south of the Sahara. Zambia's 1985 population is estimated to be 6.8 million. Between 1963 and 1969, the average annual population growth rate was 2.5: it was 3.1% between 1969-80. The current birthrate of about 48/1000 is expected to decline only marginally in the next 15 years, but the death rate is declining more rapidly -- from 19/1000 in the late 1960s to 15/1000 in 1985. Life expectancy is expected to rise from the current 51 years to about 58 years. As a result of the high growth rate, Zambia's population is young, with a median age of about 16.3 years. Traditional African values stress the importance of large families. Zambia's total fertility rate was 6.9 in 1985. According to the World Bank, only 1% of married women of childbearing age in 1982 used contraceptives. Although tribal links are weakening, Zambia still counts 73 officially recognized tribes

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

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…

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

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.

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

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.

e-Government for Development Information Exchange (DIE): Zambia

NASA Astrophysics Data System (ADS)

Joseph, Bwalya Kelvin

In most parts of the world, political systems which utilize authoritative rule and mostly employ top-down decision-making processes are slowly transcending towards democratic norms. Information Technology Systems have been identified and adopted as one of the most efficient vehicles for appropriate, transparent and inclusive / participatory decision making. Zambia has shown a higher propensity to indigenous knowledge systems which are full of inefficiencies, a lot of red tape in public service delivery, and prone to corrupt practices. Despite that being the case, it is slowly trying to implement e-government. The adoption of e-government promises a sharp paradigm shift where public institutions will be more responsive and transparent, promote efficient PPP (Public Private Partnerships), and empower citizens by making knowledge and other resources more directly accessible. This paper examines three cases from Zambia where ICT in support of e-government has been implemented for Development Information Exchange (DIE) - knowledge-based decision making. The paper also assesses the challenges, opportunities, and issues together with e-government adoption criteria regarding successful encapsulation of e-government into the Zambian contextual environment. I propose a conceptual model which offers balanced e-government adoption criteria involving a combination of electronic and participatory services. This conceptual e-government adoption model can later be replicated to be used at the Southern African Development Community (SADC) level given the similarity in the contextual environment.

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.

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…

Elites, Incrementalism and Educational Policy-Making in Post-Independence Zambia.

ERIC Educational Resources Information Center

Lungu, Gatian F.

1985-01-01

Examines the role of elite groups in Zambia educational policymaking in the postindependence era, using three major attempts at educational reform as illustrations. Concludes that well-to-do groups have dominated educational policy decisions to preserve their own interests and have obtained gradual reforms in spite of offically declared radical…

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.

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.

[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.

Catholic Schools in Zambia: 1891-1924.

ERIC Educational Resources Information Center

Carmody, Brendan

1999-01-01

Retraces the contribution of the Catholic Church to schooling in Northern Rhodesia (currently Zambia) from 1891-1924. Provides background on the development of the Church in Zambia. Discusses Catholic and government perspectives on schooling and conversion, Catholic schooling in Zambia, and the African response to Catholic schooling. (CMK)

[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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

Assessing School Readiness for a Practice Arrangement Using Decision Tree Methodology.

ERIC Educational Resources Information Center

Barger, Sara E.

1998-01-01

Questions in a decision-tree address mission, faculty interest, administrative support, and practice plan as a way of assessing arrangements for nursing faculty's clinical practice. Decisions should be based on congruence between the human resource allocation and the reward systems. (SK)

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…

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

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.

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.

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.

A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.

PubMed

Nair, Shalini Rajandran; Tan, Li Kuo; Mohd Ramli, Norlisah; Lim, Shen Yang; Rahmat, Kartini; Mohd Nor, Hazman

2013-06-01

To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD). 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3. Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified. Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

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-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.

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.

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.

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.

Restructuring of labor markets in the Philippines and Zambia: the gender dimension.

PubMed

Floro, M S; Schaefer, K

1998-01-01

, thereby challenging the governing pattern of income control and decision making. Thus, the economic restructuring of the Philippines and Zambia did not necessarily bring about significant changes in the labor market such that gender equality would be promoted.

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.

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.

HMIS and decision-making in Zambia: re-thinking information solutions for district health management in decentralized health systems.

PubMed

Mutemwa, Richard I

2006-01-01

At the onset of health system decentralization as a primary health care strategy, which constituted a key feature of health sector reforms across the developing world, efficient and effective health management information systems (HMIS) were widely acknowledged and adopted as a critical element of district health management strengthening programmes. The focal concern was about the performance and long-term sustainability of decentralized district health systems. The underlying logic was that effective and efficient HMIS would provide district health managers with the information required to make effective strategic decisions that are the vehicle for district performance and sustainability in these decentralized health systems. However, this argument is rooted in normative management and decision theory without significant unequivocal empirical corroboration. Indeed, extensive empirical evidence continues to indicate that managers' decision-making behaviour and the existence of other forms of information outside the HMIS, within the organizational environment, suggest a far more tenuous relationship between the presence of organizational management information systems (such as HMIS) and effective strategic decision-making. This qualitative comparative case-study conducted in two districts of Zambia focused on investigating the presence and behaviour of five formally identified, different information forms, including that from HMIS, in the strategic decision-making process. The aim was to determine the validity of current arguments for HMIS, and establish implications for current HMIS policies. Evidence from the eight strategic decision-making processes traced in the study confirmed the existence of different forms of information in the organizational environment, including that provided by the conventional HMIS. These information forms attach themselves to various organizational management processes and key aspects of organizational routine. The study results point

Data for developing allometric models and evaluating carbon stocks of the Zambezi Teak Forests in Zambia.

PubMed

Ngoma, Justine; Moors, Eddy; Kruijt, Bart; Speer, James H; Vinya, Royd; Chidumayo, Emmanuel N; Leemans, Rik

2018-04-01

This paper presents data on carbon stocks of tropical tree species along a rainfall gradient. The data was generated from the Sesheke, Namwala, and Kabompo sites in Zambia. Though above-ground data was generated for all these three sites, we uprooted trees to determine below-ground biomass from the Sesheke site only. The vegetation was assessed in all three sites. The data includes tree diameter at breast height (DBH), total tree height, wood density, wood dry weight and root dry weight for large (≥ 5 cm DBH) and small (< 5 cm DBH) trees. We further presented Root-to-Shoot Ratios of uprooted trees. Data on the importance-value indices of various species for large and small trees are also determined. Below and above-ground carbon stocks of the surveyed tree species are presented per site. This data were used by Ngoma et al. (2018) [1] to develop above and below-ground biomass models and the reader is referred to this study for additional information, interpretation, and reflection on applying this data.

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.

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.

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.

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.

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.

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.

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.

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.

Ventriculogram segmentation using boosted decision trees

NASA Astrophysics Data System (ADS)

McDonald, John A.; Sheehan, Florence H.

2004-05-01

Left ventricular status, reflected in ejection fraction or end systolic volume, is a powerful prognostic indicator in heart disease. Quantitative analysis of these and other parameters from ventriculograms (cine xrays of the left ventricle) is infrequently performed due to the labor required for manual segmentation. None of the many methods developed for automated segmentation has achieved clinical acceptance. We present a method for semi-automatic segmentation of ventriculograms based on a very accurate two-stage boosted decision-tree pixel classifier. The classifier determines which pixels are inside the ventricle at key ED (end-diastole) and ES (end-systole) frames. The test misclassification rate is about 1%. The classifier is semi-automatic, requiring a user to select 3 points in each frame: the endpoints of the aortic valve and the apex. The first classifier stage is 2 boosted decision-trees, trained using features such as gray-level statistics (e.g. median brightness) and image geometry (e.g. coordinates relative to user supplied 3 points). Second stage classifiers are trained using the same features as the first, plus the output of the first stage. Border pixels are determined from the segmented images using dilation and erosion. A curve is then fit to the border pixels, minimizing a penalty function that trades off fidelity to the border pixels with smoothness. ED and ES volumes, and ejection fraction are estimated from border curves using standard area-length formulas. On independent test data, the differences between automatic and manual volumes (and ejection fractions) are similar in size to the differences between two human observers.

[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.

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.

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.

Leprosy trends in Zambia 1991-2009.

PubMed

Kapata, Nathan; Chanda-Kapata, Pascalina; Grobusch, Martin Peter; O'Grady, Justin; Bates, Matthew; Mwaba, Peter; Zumla, Alimuddin

2012-10-01

To document leprosy trends in Zambia over the past two decades to ascertain the importance of leprosy as a health problem in Zambia. Retrospective study covering the period 1991-2009 of routine national leprosy surveillance data, published national programme review reports and desk reviews of in-country TB reports. Data reports were available for all the years under study apart from years 2001, 2002 and 2006. The Leprosy case notification rates (CNR) declined from 2.73/10 000 population in 1991 to 0.43/10 000 population in 2009. The general leprosy burden showed a downward trend for both adults and children. Leprosy case burden dropped from approximately 18 000 cases in 1980 to only about 1000 cases in 1996, and by the year 2000, the prevalence rates had fallen to 0.67/10 000 population. There were more multibacillary cases of leprosy than pauci-bacillary cases. Several major gaps in data recording, entry and surveillance were identified. Data on disaggregation by gender, HIV status or geographical origin were not available. Whilst Zambia has achieved WHO targets for leprosy control, leprosy prevalence data from Zambia may not reflect real situation because of poor data recording and surveillance. Greater investment into infrastructure and training are required for more accurate surveillance of leprosy in Zambia. © 2012 Blackwell Publishing Ltd.

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.

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.

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.

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.

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

Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology.

ERIC Educational Resources Information Center

Chen, Gwo-Dong; Liu, Chen-Chung; Ou, Kuo-Liang; Liu, Baw-Jhiune

2000-01-01

Discusses the use of Web logs to record student behavior that can assist teachers in assessing performance and making curriculum decisions for distance learning students who are using Web-based learning systems. Adopts decision tree and data cube information processing methodologies for developing more effective pedagogical strategies. (LRW)

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.

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

Mental illness--stigma and discrimination in Zambia.

PubMed

Kapungwe, A; Cooper, S; Mwanza, J; Mwape, L; Sikwese, A; Kakuma, R; Lund, C; Flisher, A J

2010-07-01

The aim of this qualitative study was to explore the presence, causes and means of addressing individual and systemic stigma and discrimination against people with mental illness in Zambia. This is to facilitate the development of tailor-made antistigma initiatives that are culturally sensitive for Zambia and other low-income African countries. This is the first in-depth study on mental illness stigma in Zambia. Fifty semi-structured interviews and 6 focus group discussions were conducted with key stakeholders drawn from 3 districts in Zambia (Lusaka, Kabwe and Sinazongwe). Transcripts were analyzed using a grounded theory approach. Mental illness stigma and discrimination is pervasive across Zambian society, prevailing within the general community, amongst family members, amid general and mental health care providers, and at the level of government. Such stigma appears to be fuelled by misunderstandings of mental illness aetiology; fears of contagion and the perceived dangerousness of people with mental illness; and associations between HIV/AIDS and mental illness. Strategies suggested for reducing stigma and discrimination in Zambia included education campaigns, the transformation of mental health policy and legislation and expanding the social and economic opportunities of the mentally ill. In Zambia, as in many other low-income African countries, very little attention is devoted to addressing the negative beliefs and behaviours surrounding mental illness, despite the devastating costs that ensue. The results from this study underscore the need for greater commitment from governments and policy-makers in African countries to start prioritizing mental illness stigma as a major public health and development issue.

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

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.

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.

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

Distribution and phenology of ixodid ticks in southern Zambia.

PubMed

Speybroeck, N; Madder, M; Van Den Bossche, P; Mtambo, J; Berkvens, N; Chaka, G; Mulumba, M; Brandt, J; Tirry, L; Berkvens, D

2002-12-01

Distribution data for epidemiologically important ticks (Acari: Ixodidae) in the Southern Province of Zambia, one of the main cattle areas of the country, are presented. Boophilus microplus (Canestrini) was not recorded in southern Zambia, whereas Boophilus decoloratus (Koch) is present throughout the area. New distribution patterns for less economically important ixodid ticks are also discussed. Southern Zambia is a transition zone because it is the most northern area in Africa where mixed Rhipicephalus appendiculatus Neumann and Rhipicephalus zambeziensis Walker, Norval & Corwin populations were reported. Although a second generation of adult R. appendiculatus/R. zamnbeziensis was encountered, simulations indicated that this phenomenon is very rare in southern Zambia, mainly because of the colder temperatures during the early dry season and lower rainfall. These simulations were supported by a development trial under experimental conditions. Tick body size measurements showed that southern Zambian ticks are larger than eastern Zambian R. appendiculatus. It is hypothesized that body size is related to diapausing intensity in this species. The epidemiological consequences are that a different approach to control Theileria parva (Theiler) (Piroplasmida: Theileriidae) and other tick-borne diseases is needed in southern Zambia, compared to the one adopted in eastern Zambia.

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.

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.

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.

Extracting decision rules from police accident reports through decision trees.

PubMed

de Oña, Juan; López, Griselda; Abellán, Joaquín

2013-01-01

Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using decision trees to understand structure in missing data

PubMed Central

Tierney, Nicholas J; Harden, Fiona A; Harden, Maurice J; Mengersen, Kerrie L

2015-01-01

Objectives Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting Data taken from employees at 3 different industrial sites in Australia. Participants 7915 observations were included. Materials and methods The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the number of visits, and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured as compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusions Researchers are encouraged to use CART and BRT models to explore and understand missing data. PMID:26124509

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

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.

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.

Facilitators and barriers for HIV-testing in Zambia: A systematic review of multi-level factors.

PubMed

Qiao, Shan; Zhang, Yao; Li, Xiaoming; Menon, J Anitha

2018-01-01

It was estimated that 1.2 million people live with HIV/AIDS in Zambia by 2015. Zambia has developed and implemented diverse programs to reduce the prevalence in the country. HIV-testing is a critical step in HIV treatment and prevention, especially among all the key populations. However, there is no systematic review so far to demonstrate the trend of HIV-testing studies in Zambia since 1990s or synthesis the key factors that associated with HIV-testing practices in the country. Therefore, this study conducted a systematic review to search all English literature published prior to November 2016 in six electronic databases and retrieved 32 articles that meet our inclusion criteria. The results indicated that higher education was a common facilitator of HIV testing, while misconception of HIV testing and the fear of negative consequences were the major barriers for using the testing services. Other factors, such as demographic characteristics, marital dynamics, partner relationship, and relationship with the health care services, also greatly affects the participants' decision making. The findings indicated that 1) individualized strategies and comprehensive services are needed for diverse key population; 2) capacity building for healthcare providers is critical for effectively implementing the task-shifting strategy; 3) HIV testing services need to adapt to the social context of Zambia where HIV-related stigma and discrimination is still persistent and overwhelming; and 4) family-based education and intervention should involving improving gender equity.

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.

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

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.

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.

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.

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.

Addressing HIV in Zambia through traditional games.

PubMed

Njelesani, Janet; Njelesani, Donald

2018-05-18

There has been a proliferation of organizations in Zambia touting the mobilization of traditional games as a tool to prevent HIV. However, there is a dearth of evidence on how culturally important activities like traditional games are being incorporated into programing. The purpose of this study was to explore how traditional games are used as a strategy to prevent HIV in Zambia. This qualitative study generated data from 17 case studies of HIV programs operating in Lusaka, Zambia. Observations of the programs were conducted and 44 interviews with program staff were completed. Participants believed that traditional games can engage youth while helping them learn about HIV. However, when traditional games were implemented, they were oversimplified and taught via regimented practices that did not foster critical thinking. This kind of implementation comes at the expense of the development of skills needed to retain and act on information essential for HIV prevention. The results of the study also reveal that due to the increase in cultural pride that has welcomed the revival of traditional games, there are opportunities to encourage government and political support for their systematic integration to address HIV in Zambia.

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.

Decentralization in Zambia: resource allocation and district performance.

PubMed

Bossert, Thomas; Chitah, Mukosha Bona; Bowser, Diana

2003-12-01

Zambia implemented an ambitious process of health sector decentralization in the mid 1990s. This article presents an assessment of the degree of decentralization, called 'decision space', that was allowed to districts in Zambia, and an analysis of data on districts available at the national level to assess allocation choices made by local authorities and some indicators of the performance of the health systems under decentralization. The Zambian officials in health districts had a moderate range of choice over expenditures, user fees, contracting, targeting and governance. Their choices were quite limited over salaries and allowances and they did not have control over additional major sources of revenue, like local taxes. The study found that the formula for allocation of government funding which was based on population size and hospital beds resulted in relatively equal per capita expenditures among districts. Decentralization allowed the districts to make decisions on internal allocation of resources and on user fee levels and expenditures. General guidelines for the allocation of resources established a maximum and minimum percentage to be allocated to district offices, hospitals, health centres and communities. Districts tended to exceed the maximum for district offices, but the large urban districts and those without public district hospitals were not even reaching the minimum for hospital allocations. Wealthier and urban districts were more successful in raising revenue through user fees, although the proportion of total expenditures that came from user fees was low. An analysis of available indicators of performance, such as the utilization of health services, immunization coverage and family planning activities, found little variation during the period 1995-98 except for a decline in immunization coverage, which may have also been affected by changes in donor funding. These findings suggest that decentralization may not have had either a positive or

Peace Corps/Zambia PST 1995 Special Lessons. Nyanja.

ERIC Educational Resources Information Center

Peace Corps (Zambia).

This guide is designed for language teachers training Peace Corps volunteers in Nyanja for service in Zambia, and focuses on daily communication skills in that context. It consists of a language "survival kit" of useful phrases and vocabulary, conjugation of the verb "to be," the Zambia national anthem, extensive notes on verb…

Nontuberculous Mycobacteria, Zambia

PubMed Central

van der Sande, Marianne A.B.; de Graaff, Cas S.; Parkinson, Shelagh; Verbrugh, Henri A.; Petit, Pieter L.C.; van Soolingen, Dick

2009-01-01

Clinical relevance of nontuberculous mycobacteria (NTM) isolated from 180 chronically ill patients and 385 healthy controls in Zambia was evaluated to examine the contribution of these isolates to tuberculosis (TB)–like disease. The proportion of NTM-positive sputum samples was significantly higher in the patient group than in controls; 11% and 6%, respectively (p<0.05). NTM-associated lung disease was diagnosed for 1 patient, and a probable diagnosis was made for 3 patients. NTM-positive patients and controls were more likely to report vomiting and diarrhea and were more frequently underweight than the NTM-negative patients and controls. Chest radiographs of NTM-positive patients showed deviations consistent with TB more frequently than those of controls. The most frequently isolated NTM was Mycobacterium avium complex. Multiple, not previously identified mycobacteria (55 of 171 NTM) were isolated from both groups. NTM probably play an important role in the etiology of TB-like diseases in Zambia. PMID:19193268

The institutional context of tobacco production in Zambia.

PubMed

Labonté, Ronald; Lencucha, Raphael; Drope, Jeffrey; Packer, Corinne; Goma, Fastone M; Zulu, Richard

2018-01-16

Tobacco production is said to be an important contributor to Zambia's economy in terms of labour and revenue generation. In light of Zambia's obligations under the WHO Framework Convention of Tobacco Control (FCTC) we examined the institutional actors in Zambia's tobacco sector to better understand their roles and determine the institutional context that supports tobacco production in Zambia. Findings from 26 qualitative, semi-structured individual or small-group interviews with key informants from governmental, intergovernmental and non-governmental organisations were analysed, along with data and information from published literature. Although Zambia is obligated under the FCTC to take steps to reduce tobacco production, the country's weak economy and strong tobacco interests make it difficult to achieve this goal. Respondents uniformly acknowledged that growing the country's economy and ensuring employment for its citizens are the government's top priorities. Lacklustre coordination and collaboration between the institutional actors, both within and outside government, contributes to an environment that helps sustain tobacco production in the country. A Tobacco Products Control Bill has been under review for a number of years, but with no supply measures included, and with no indication of when or whether it will be passed. As with other low-income countries involved in tobacco production, there is inconsistency between Zambia's economic policy to strengthen the country's economy and its FCTC commitment to regulate and control tobacco production. The absence of a whole-of-government approach towards tobacco control has created an institutional context of duelling objectives, with some government ministries working at cross-purposes and tobacco interests left unchecked. With no ultimate coordinating authority, this industry risks being run according to the desire and demands of multinational tobacco companies, with few, if any, checks against them.

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.

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.

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.

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

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.

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.

Cigarette Price and Other Factors Associated with Brand Choice and Brand Loyalty in Zambia: Findings from the ITC Zambia Survey

PubMed Central

Salloum, Ramzi G.; Goma, Fastone; Chelwa, Grieve; Cheng, Xi; Zulu, Richard; Kaai, Susan C.; Quah, Anne C.K.; Thrasher, James F.; Fong, Geoffrey T.

2015-01-01

Objectives Little is known about cigarette pricing and brand loyalty in sub-Saharan Africa. This study examines these issues in Zambia, analyzing data from the International Tobacco Control (ITC) Zambia Survey. Methods Data from Wave 1 of the ITC Zambia Survey (2012) were analyzed for current smokers of factory-made (FM) cigarettes compared to those who smoked both FM and roll-your-own (RYO) cigarettes, using multivariate logistic regression models to identify the predictors of brand loyalty and reasons for brand choice. Results 75% of FM-only smokers and 64% of FM+RYO smokers reported having a regular brand. Compared with FM-only smokers, FM+RYO smokers were, on average, older (28% vs. 20% ≥ 40 years), low income (64% vs. 43%), and had lower education (76% vs. 44% < secondary). Mean price across FM brands was ZMW0.50 (USD0.08) per stick. Smokers were significantly less likely to be brand-loyal (>1 year) if they were aged 15-17 years (vs. 40-54 years) and if they had moderate (vs. low) income. Brand choice was predicted mostly by friends, taste, and brand popularity. Price was more likely to be a reason for brand loyalty among FM+RYO smokers, among ≥55 year old smokers, and among those who reported being more addicted to cigarettes. Conclusions These results in Zambia document the high levels of brand loyalty in a market where price variation is fairly small across cigarette brands. Future research is needed on longitudinal trends to evaluate the effect of tobacco control policies in Zambia. PMID:25631482

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.

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.

Will savannas survive outside the parks? A lesson from Zambia

NASA Astrophysics Data System (ADS)

Kutsch, W.; Merbold, L.; Scholes, B.; Mukelabai, M.

2012-04-01

Miombo woodlands cover the transition zone between dry open savannas and moist forests in Southern Africa. They cover about 2.7 million km2 in southern Africa and provide many ecosystem services that support rural life, including medical products, wild foods, construction timber and fuel. In Zambia, as in many of its neighbouring countries, miombo woodlands are currently experiencing accelerating degradation and clearing, mostly with charcoal production as the initial driver. Domestic energy needs in the growing urban areas are largely satisfied by charcoal, which is less energy-efficient fuel on a tree-to-table basis than the firewood that is used in rural areas, but has a higher energy density and is thus cheaper to transport. This study uses data from inventories and from eddy covariance measurements of carbon exchange to characterize the impact of charcoal production on miombo woodlands. We address the following questions: (i) how much carbon is lost at local as well as at national scale and (ii) does forest degradation result in the loss of a carbon sink? On the basis of our data we (iii) estimate the per capita emissions through deforestation and forest degradation in Zambia and relate it to fossil fuel emissions. Furthermore, (iv) a rough estimate of the energy that is provided by charcoal production to private households at a national level is calculated and (v) options for alternative energy supply to private households are discussed.

Perceptions of and Attitudes towards Ageing in Zambia

ERIC Educational Resources Information Center

Mapoma, Christopher C.; Masaiti, Gift

2012-01-01

This paper reflects part of the wider outlook on ageing in general in Zambia and was intended to investigate perceptions of and attitudes towards the aged and ageing in Zambia by members of the community who, by definition and chronologically are not classified as aged i.e. not yet 60 years and over. Focus Group Discussions (FGD) were used to…

Attitudes toward abortion in Zambia.

PubMed

Geary, Cynthia Waszak; Gebreselassie, Hailemichael; Awah, Paschal; Pearson, Erin

2012-09-01

Despite Zambia's relatively progressive abortion law, women continue to seek unsafe, illegal abortions. Four domains of abortion attitudes - support for legalization, immorality, rights, and access to services - were measured in 4 communities. A total of 668 people were interviewed. Associations among the 4 domains were inconsistent with expectations. The belief that abortion is immoral was widespread, but was not associated with lack of support for legalization. Instead, it was associated with belief that women need access to safe services. These findings suggest that increasing awareness about abortion law in Zambia may be important for encouraging more favorable attitudes. Copyright © 2012 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Acquisition of Scientific Literature in Developing Countries. 4: Zambia.

ERIC Educational Resources Information Center

Lundu, Maurice C.; Lungu, Charles B. M.

1989-01-01

Description of selected science and technical libraries and information services in Zambia focuses on collection development and acquisition policies. The problems of transferring technology through the transfer of information are discussed, the future of information transfer in Zambia is explored, and proposals for future action are presented.…

Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

NASA Astrophysics Data System (ADS)

Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

2018-05-01

Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

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.

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.

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.

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

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 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....

Cigarette price and other factors associated with brand choice and brand loyalty in Zambia: findings from the ITC Zambia Survey.

PubMed

Salloum, Ramzi G; Goma, Fastone; Chelwa, Grieve; Cheng, Xi; Zulu, Richard; Kaai, Susan C; Quah, Anne C K; Thrasher, James F; Fong, Geoffrey T

2015-07-01

Little is known about cigarette pricing and brand loyalty in sub-Saharan Africa. This study examines these issues in Zambia, analysing data from the International Tobacco Control (ITC) Zambia Survey. Data from Wave 1 of the ITC Zambia Survey (2012) were analysed for current smokers of factory-made (FM) cigarettes compared with those who smoked both FM and roll-your-own (RYO) cigarettes, using multivariate logistic regression models to identify the predictors of brand loyalty and reasons for brand choice. 75% of FM-only smokers and 64% of FM+RYO smokers reported having a regular brand. Compared with FM-only smokers, FM+RYO smokers were, on average, older (28% vs 20% ≥40 years), low income (64% vs 43%) and had lower education (76% vs 44% < secondary). Mean price across FM brands was ZMW0.50 (US$0.08) per stick. Smokers were significantly less likely to be brand loyal (>1 year) if they were aged 15-17 years (vs 40-54 years) and if they had moderate (vs low) income. Brand choice was predicted mostly by friends, taste and brand popularity. Price was more likely to be a reason for brand loyalty among FM+RYO smokers, among ≥55-year-old smokers and among those who reported being more addicted to cigarettes. These results in Zambia document the high levels of brand loyalty in a market where price variation is fairly small across cigarette brands. Future research is needed on longitudinal trends to evaluate the effect of tobacco control policies in Zambia. 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.

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…

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.

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

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.

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.

Theileriosis in Zambia: etiology, epidemiology and control measures.

PubMed

Nambota, A; Samui, K; Sugimoto, C; Kakuta, T; Onuma, M

1994-06-01

In Zambia, theileriosis manifests itself in the form of Corridor disease (CD), caused by Theileria parva lawrencei, and East Coast fever (ECF), caused by T. parva parva. Of the approximately 3 million cattle in Zambia, 1.4 million are at risk to theileriosis. ECF is found in the Northern and Eastern provinces of the country, while CD appears in Southern, Central, Lusaka and Copperbelt provinces. Theileriosis is a major constraint to the development of the livestock industry in Zambia, with losses of about 10,000 cattle per annum. The disease is spreading at a very fast rate, over-flowing its original borders. The epidemiology is complicated by, among other factors, the wide distribution of the tick vector, Rhipicephalus appendiculatus, which is found all over the country. The current strategy of relying on tick control and therapeutic drugs as a way of controlling the disease is becoming increasingly difficult for Zambia. This is because both curative drugs and acaricides are very costly. Immunization against theileriosis using the infection and treatment method as a way of controlling the disease is becoming increasingly accepted, provided local Theileria stocks are used. This paper reviews the incidence of theileriosis in the last 2 years, 1991 and 1992. It also gives a historical perspective of the disease, epidemiology and control measures presently in use.

Can family planning outreach bridge the urban-rural divide in Zambia?

PubMed

White, Justin S; Speizer, Ilene S

2007-09-05

Zambia experienced declining aggregate fertility and increasing aggregate contraceptive use from 1990 to 2000. Yet, in rural Zambia, progress in family planning has lagged far behind the advances made in Zambia's urban areas. The contraceptive prevalence rate in Lusaka and other urban areas outstripped the rate in rural Zambia by nearly 25 percentage points (41.2 percent versus 16.6 percent) in 2001. The total fertility rate varied between urban and rural areas by 2.5 children (4.3 versus 6.9 children). This paper considers the urban-rural differentials in Zambia and assesses family planning outreach as a tool to narrow this divide. This study uses the Zambia Demographic and Health Survey (DHS) data, collected between 2001 and 2002. Logistic regression techniques were employed to examine factors associated with contraceptive use. The first analysis tested modern contraceptive use versus traditional method use and no use. In addition, separate models were run for samples stratified by type of residence (rural or urban) to determine if different factors were associated with use by residence. A simulation determined the effect of all women receiving at least one household visit from a health worker if all other variables were held constant. Differences in modern contraceptive use between urban and rural areas persist (OR: 1.56, 95 percent CI: 1.24-1.96) even after adjusting for a number of demographic, socioeconomic, cognitive, and attitudinal factors. Household visits by a community health worker significantly increased the likelihood of modern contraceptive use among rural women (OR: 1.83; 95 percent CI: 1.29-2.58). If all rural women received at least one outreach visit per year, the prevalence rate for modern contraceptive methods would be expected to increase for this group by 5.9 percentage points, a marked increase but less than one-quarter of the total urban-rural differential. Outreach in the form of health worker visits can improve access to family planning

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.

Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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

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.

Recasting Postcolonial Citizenship through Civic Education: Critical Perspectives on Zambia

ERIC Educational Resources Information Center

Abdi, Ali A.; Shizha, Edward; Bwalya, Ignatio

2006-01-01

Since the early 1990s and, perhaps, as one effect of the emergence of the uni-polar world, there have been a lot of "democratizing" activities in the Sub-Saharan context, with Zambia, a central African country of about 10 million, at the forefront of these processes. While democracy, in one form or another, has come to Zambia,…

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...

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.

Further evidence for geographic differentiation in R. appendiculatus (Acari: Ixodidae) from Eastern and Southern provinces of Zambia.

PubMed

Mtambo, Jupiter; Madder, Maxime; Van Bortel, Wim; Chaka, George; Berkvens, Dirk; Backeljau, Thierry

2007-01-01

Studies in the biology, ecology and behaviour of R. appendiculatus in Zambia have shown considerable variation within and between populations often associated with their geographical origin. We studied variation in the mitochondrial COI (mtCOI) gene of adult R. appendiculatus ticks originating from the Eastern and Southern provinces of Zambia. Rhipicephalus appendiculatus ticks from the two provinces were placed into two groups on the mtCOI sequence data tree. One group comprised all haplotypes of specimens from the Eastern province plateau districts of Chipata and Petauke. The second group consisted of a single haplotype of specimens from the Southern province districts and Nyimba, an Eastern province district on the fringes of the valley. This variation provides additional evidence to the earlier observations in the 12S rDNA and ITS2 data for the geographic subdivision of R. appendiculatus from Southern province and Eastern province plateau. The geographic subdivision further corresponds with differences in body size and diapause between R. appendiculatus from these geographic areas. The possible implications of these findings on the epidemiology of East Coast fever (ECF) the disease for which R. appendiculatus is one of the vectors are discussed.

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.

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.

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.

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.

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.

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.

Technical and scale efficiency in the delivery of child health services in Zambia: results from data envelopment analysis.

PubMed

Achoki, Tom; Hovels, Anke; Masiye, Felix; Lesego, Abaleng; Leufkens, Hubert; Kinfu, Yohannes

2017-01-05

Despite tremendous efforts to scale up key maternal and child health interventions in Zambia, progress has not been uniform across the country. This raises fundamental health system performance questions that require further investigation. Our study investigates technical and scale efficiency (SE) in the delivery of maternal and child health services in the country. The study focused on all 72 health districts of Zambia. We compiled a district-level database comprising health outcomes (measured by the probability of survival to 5 years of age), health outputs (measured by coverage of key health interventions) and a set of health system inputs, namely, financial resources and human resources for health, for the year 2010. We used data envelopment analysis to assess the performance of subnational units across Zambia with respect to technical and SE, controlling for environmental factors that are beyond the control of health system decision makers. Nationally, average technical efficiency with respect to improving child survival was 61.5% (95% CI 58.2% to 64.8%), which suggests that there is a huge inefficiency in resource use in the country and the potential to expand services without injecting additional resources into the system. Districts that were more urbanised and had a higher proportion of educated women were more technically efficient. Improved cooking methods and donor funding had no significant effect on efficiency. With the pressing need to accelerate progress in population health, decision makers must seek efficient ways to deliver services to achieve universal health coverage. Understanding the factors that drive performance and seeking ways to enhance efficiency offer a practical pathway through which low-income countries could improve population health without necessarily seeking additional resources. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Technical and scale efficiency in the delivery of child health services in Zambia: results from data envelopment analysis

PubMed Central

Achoki, Tom; Hovels, Anke; Masiye, Felix; Lesego, Abaleng; Leufkens, Hubert; Kinfu, Yohannes

2017-01-01

Objective Despite tremendous efforts to scale up key maternal and child health interventions in Zambia, progress has not been uniform across the country. This raises fundamental health system performance questions that require further investigation. Our study investigates technical and scale efficiency (SE) in the delivery of maternal and child health services in the country. Setting The study focused on all 72 health districts of Zambia. Methods We compiled a district-level database comprising health outcomes (measured by the probability of survival to 5 years of age), health outputs (measured by coverage of key health interventions) and a set of health system inputs, namely, financial resources and human resources for health, for the year 2010. We used data envelopment analysis to assess the performance of subnational units across Zambia with respect to technical and SE, controlling for environmental factors that are beyond the control of health system decision makers. Results Nationally, average technical efficiency with respect to improving child survival was 61.5% (95% CI 58.2% to 64.8%), which suggests that there is a huge inefficiency in resource use in the country and the potential to expand services without injecting additional resources into the system. Districts that were more urbanised and had a higher proportion of educated women were more technically efficient. Improved cooking methods and donor funding had no significant effect on efficiency. Conclusions With the pressing need to accelerate progress in population health, decision makers must seek efficient ways to deliver services to achieve universal health coverage. Understanding the factors that drive performance and seeking ways to enhance efficiency offer a practical pathway through which low-income countries could improve population health without necessarily seeking additional resources. PMID:28057650

A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure

PubMed Central

Goh, Patrick K.; Doyle, Lauren R.; Glass, Leila; Jones, Kenneth L.; Riley, Edward P.; Coles, Claire D.; Hoyme, H. Eugene; Kable, Julie A.; May, Philip A.; Kalberg, Wendy O.; Elizabeth, R. Sowell; Wozniak, Jeffrey R.; Mattson, Sarah N.

2017-01-01

Objective To develop and validate a hierarchical decision tree model, combining neurobehavioral and physical measures, for identification of children affected by prenatal alcohol exposure even when facial dysmorphology is not present. Study design Data were collected as part of a multisite study across the United States. The model was developed after evaluating over 1000 neurobehavioral and dysmorphology variables collected from 434 children (8–16y) with prenatal alcohol exposure, with and without fetal alcohol syndrome (FAS), and non-exposed controls, with and without other clinically-relevant behavioral or cognitive concerns. The model was subsequently validated in an independent sample of 454 children in two age ranges (5–7y or 10–16y). In all analyses, the discriminatory ability of each model step was tested with logistic regression. Classification accuracies and positive and negative predictive values were calculated. Results The model consisted of variables from 4 measures (2 parent questionnaires, an IQ score, and a physical examination). Overall accuracy rates for both the development and validation samples met or exceeded our goal of 80% overall accuracy. Conclusions The decision tree model distinguished children affected by prenatal alcohol exposure from non-exposed controls, including those with other behavioral concerns or conditions. Improving identification of this population will streamline access to clinical services, including multidisciplinary evaluation and treatment. PMID:27476634

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

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.

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).

Consultancy Report: Assessment of the Zambia College of Distance Education (ZACODE)

ERIC Educational Resources Information Center

Ellis, Justin

2009-01-01

This study was carried out at the request of the Ministry of Education, Zambia. The Commonwealth of Learning contracted Turning Points Consultancy CC, a Namibian company, who provided the services of the author, to "carry out an evaluation of the Zambia College of Distance Education (ZACODE) and submit recommendations to the Ministry of…

77 FR 60966 - Executive-Led Trade Mission to South Africa and Zambia

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-05

... Africa and Zambia AGENCY: International Trade Administration, Department of Commerce. ACTION: Notice...- Led Trade Mission to South Africa and Zambia scheduled for November 26- 30, 2012, to revise the dates... and scheduling constraints permit), interested U.S. agriculture, mining, transportation, water, energy...

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.

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.

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.

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.

Structural adjustment and drought in Zambia.

PubMed

Mulwanda, M

1995-06-01

While drought is not uncommon in Zambia, the country is now facing the worst drought in history. The monetary and social costs will be enormous. Although it is too early to measure the economic and social costs of the drought on Zambia, it is obvious that the impact is catastrophic on a country whose economy is under pressure. The drought will affect the structural adjustment programme (SAP) unveiled by the new government which has embraced the market economy. The country has imported, and will continue to import, large quantities of maize and other foodstuffs, a situation likely to strain the balance of payments. Earlier targets with regard to export earnings, reductions in the budget deficit, and GDP growth as contained in the Policy Framework Paper (PFP) are no longer attainable due to the effects of the drought.

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

Zambia: Multi-Faith Religious Education?

ERIC Educational Resources Information Center

Carmody, Brendan

2006-01-01

As countries' populations become more religiously diverse, a need to review the religious education syllabus that operates is often perceived. One such country is Zambia, which was not only traditionally religiously diverse but has become even more so with the advent of Christianity, Islam and Hinduism and other non-African faiths. This article…

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the

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

Urbanization in Zambia. An International Urbanization Survey Report to the Ford Foundation.

ERIC Educational Resources Information Center

Simmance, Alan J. F.

This report reviews the "Seers Report," which contained policy guidelines for modern development planning in Zambia, and compares its findings to recent findings during the period 1963-1970. The Seers Report found that Zambia was the most urbanized country in Africa south of the Sahara (excluding South Africa). This report finds that…

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

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 .

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.

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...

Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

PubMed

Chen, Weisheng; Sun, Cheng; Wei, Ru; Zhang, Yanlin; Ye, Heng; Chi, Ruibin; Zhang, Yichen; Hu, Bei; Lv, Bo; Chen, Lifang; Zhang, Xiunong; Lan, Huilan; Chen, Chunbo

2016-08-31

Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion. © 2016 American Society for Parenteral and Enteral Nutrition.

Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

PubMed

Chen, Weisheng; Sun, Cheng; Wei, Ru; Zhang, Yanlin; Ye, Heng; Chi, Ruibin; Zhang, Yichen; Hu, Bei; Lv, Bo; Chen, Lifang; Zhang, Xiunong; Lan, Huilan; Chen, Chunbo

2018-01-01

Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion. © 2016 American Society for Parenteral and Enteral Nutrition.

"Lazy men", time-use, and rural development in Zambia.

PubMed

Whitehead, A

1999-11-01

This paper examines how work and the labor in agriculture in rural sub-Saharan Africa is measured. Section 1 presents a historical example of colonial discourses of the "lazy" African (the Lamba in Zambia). Section 2 analyzes a study carried out in rural Zambia to illustrate the relationship between stereotypes held by many Europeans, particular aspects of the colonial project, and the social relations brought about by colonialism. Section 3 examines the ways in which present work and labor approaches in sub-Saharan Africa embody value judgements which leads to distorted documentation of the division of labor between opposite genders. Sections 4 through 7 look at a time-use study conducted in Zambia and argue that studies of such nature create value judgement on what comprises work, and about how researchers and planners classify this. Overall, this article has demonstrated that time-use surveys may provide inadequate understanding of women and men's work in the absence of an understanding of the local context in which the work is undertaken, and of labor markets.

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.

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.

Community attitudes towards childbearing and abortion among HIV-positive women in Nigeria and Zambia.

PubMed

Kavanaugh, Megan L; Moore, Ann M; Akinyemi, Odunayo; Adewole, Isaac; Dzekedzeke, Kumbutso; Awolude, Olutosin; Arulogun, Oyedunni

2013-01-01

Although stigma towards HIV-positive women for both continuing and terminating a pregnancy has been documented, to date few studies have examined relative stigma towards one outcome versus the other. This study seeks to describe community attitudes towards each of two possible elective outcomes of an HIV-positive woman's pregnancy - induced abortion or birth - to determine which garners more stigma and document characteristics of community members associated with stigmatising attitudes towards each outcome. Data come from community-based interviews with reproductive-aged men and women, 2401 in Zambia and 2452 in Nigeria. Bivariate and multivariate analyses revealed that respondents from both countries overwhelmingly favoured continued childbearing for HIV-positive pregnant women, but support for induced abortion was slightly higher in scenarios in which anti-retroviral therapy (ART) was unavailable. Zambian respondents held more stigmatising attitudes towards abortion for HIV-positive women than did Nigerian respondents. Women held more stigmatising attitudes towards abortion for HIV-positive women than men, particularly in Zambia. From a sexual and reproductive health and rights perspective, efforts to assist HIV-positive women in preventing unintended pregnancy and to support them in their pregnancy decisions when they do become pregnant should be encouraged in order to combat the social stigma documented in this paper.

200 junior doctors sacked in Zambia.

PubMed

Ahmad, K

2000-07-29

Since December 1999 junior doctors in Zambia have been on strike, demanding from the government better working conditions, better pay, and improvements in hospital services. However, on June 20, 2000, around 200 junior doctors were dismissed by the Zambian government, who asserts that the action was taken in the public¿s interest. Nevertheless, the doctors argue that the move came at a time when the country is struggling with a critical shortage of doctors and with an HIV/AIDS crisis. In addition, health policy experts say that the dismissal could further undermine the alarming conditions of Zambia's health care system. It is noted that there are only 800 doctors registered with the Zambian Medical Council, but WHO estimates that the country needs 1500 clinicians. To meet such a shortage, the government has hired Cuban and Chinese doctors. They are paid more and given more benefits than the Zambian doctors, generating complaints from the president of the junior doctors' representative body.

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.

Career Path Suggestion using String Matching and Decision Trees

NASA Astrophysics Data System (ADS)

Nagpal, Akshay; P. Panda, Supriya

2015-05-01

High school and college graduates seemingly are often battling for the courses they should major in order to achieve their target career. In this paper, we worked on suggesting a career path to a graduate to reach his/her dream career given the current educational status. Firstly, we collected the career data of professionals and academicians from various career fields and compiled the data set by using the necessary information from the data. Further, this was used as the basis to suggest the most appropriate career path for the person given his/her current educational status. Decision trees and string matching algorithms were employed to suggest the appropriate career path for a person. Finally, an analysis of the result has been done directing to further improvements in the model.

Bismarck in the Bush: Year 12 Write Zambia's History for Zambian Students

ERIC Educational Resources Information Center

Gray, Peter

2011-01-01

Peter Gray explains how his Year 12 students came to research and write a resource on the history of Zambia, for history teachers "in" Zambia. The construction of the resource stretched the Year 12 students in new ways: the Internet was useless and there were no easy digests in A-Level textbooks to get them started. They would have to…

7 CFR 319.56-43 - Baby corn and baby carrots from Zambia.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 5 2011-01-01 2011-01-01 false Baby corn and baby carrots from Zambia. 319.56-43... § 319.56-43 Baby corn and baby carrots from Zambia. (a) Immature, dehusked “baby” sweet corn (Zea mays L..., which is a field, where the corn has been grown must have been inspected at least once during the...

Strategies for Living with the Challenges of HIV and Antiretroviral Use in Zambia

ERIC Educational Resources Information Center

Jones, Deborah; Zulu, Isaac; Mumbi, Miriam; Chitalu, Ndashi; Vamos, Szonja; Gomez, Jacqueline; Weiss, Stephen M.

2009-01-01

This study sought to identify strategies for living with the challenges of HIV and antiretroviral (ARV) use among new medication users in urban Zambia. Participants (n = 160) were recruited from urban Lusaka, Zambia. Qualitative Data was drawn from monthly ARV treatment education intervention groups addressing HIV and antiretroviral use. Themes…

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.

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.

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

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.

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

Mwabu: Interactive Education in Zambia

ERIC Educational Resources Information Center

Gordon, Jenny; Postlewhite, Kerry

2017-01-01

Africa has more people younger than 20 years old than anywhere in the world, and the continent's population is set to double to two billion by 2050. Asking whether that is a challenge or an opportunity isn't really the right question because it is both. For Mwabu, an education technology company born and bred in Zambia, the more important question…

The Zambia Children's KS-HHV8 Study: Rationale, Study Design, and Study Methods

PubMed Central

Minhas, Veenu; Crabtree, Kay L.; Chao, Ann; Wojcicki, Janet M.; Sifuniso, Adrian M.; Nkonde, Catherine; Kankasa, Chipepo; Mitchell, Charles D.; Wood, Charles

2011-01-01

The epidemic of human immunodeficiency virus in Zambia has led to a dramatic rise in the incidence of human herpesvirus-8 (HHV-8)–associated Kaposi's sarcoma in both adults and children. However, there is a paucity of knowledge about the routes of HHV-8 transmission to young children. The Zambia Children's KS-HHV8 Study, a large, prospective cohort study in Lusaka, Zambia, was launched in 2004 to investigate the role of household members as a source of HHV-8 infection in young children and social behaviors that may modify the risk of HHV-8 acquisition. This cohort is distinct from other epidemiologic studies designed to investigate HHV-8 incidence and transmission because it recruited and followed complete households in the urban central African context. Between July 2004 and March 2007, 1,600 households were screened; 368 households comprising 464 children and 1,335 caregivers and household members were enrolled. Follow-up of this population continued for 48 months postrecruitment, affording a unique opportunity to study horizontal transmission of HHV-8 and understand the routes and sources of transmission to young children in Zambia. The authors describe the study rationale, design, execution, and characteristics of this cohort, which provides critical data on the epidemiology and transmission of HHV-8 to young children in Zambia. PMID:21447476

Aspergillus section Flavi community structure in Zambia influences aflatoxin contamination of maize and groundnut.

PubMed

Kachapulula, Paul W; Akello, Juliet; Bandyopadhyay, Ranajit; Cotty, Peter J

2017-11-16

Aflatoxins are cancer-causing, immuno-suppressive mycotoxins that frequently contaminate important staples in Zambia including maize and groundnut. Several species within Aspergillus section Flavi have been implicated as causal agents of aflatoxin contamination in Africa. However, Aspergillus populations associated with aflatoxin contamination in Zambia have not been adequately detailed. Most of Zambia's arable land is non-cultivated and Aspergillus communities in crops may originate in non-cultivated soil. However, relationships between Aspergillus populations on crops and those resident in non-cultivated soils have not been explored. Because characterization of similar fungal populations outside of Zambia have resulted in strategies to prevent aflatoxins, the current study sought to improve understanding of fungal communities in cultivated and non-cultivated soils and in crops. Crops (n=412) and soils from cultivated (n=160) and non-cultivated land (n=60) were assayed for Aspergillus section Flavi from 2012 to 2016. The L-strain morphotype of Aspergillus flavus and A. parasiticus were dominant on maize and groundnut (60% and 42% of Aspergillus section Flavi, respectively). Incidences of A. flavus L-morphotype were negatively correlated with aflatoxin in groundnut (log y=2.4990935-0.09966x, R 2 =0.79, P=0.001) but not in maize. Incidences of A. parasiticus partially explained groundnut aflatoxin concentrations in all agroecologies and maize aflatoxin in agroecology III (log y=0.1956034+0.510379x, R 2 =0.57, P<0.001) supporting A. parasiticus as the dominant etiologic agent of aflatoxin contamination in Zambia. Communities in both non-cultivated and cultivated soils were dominated by A. parasiticus (69% and 58%, respectively). Aspergillus parasiticus from cultivated and non-cultivated land produced statistically similar concentrations of aflatoxins. Aflatoxin-producers causing contamination of crops in Zambia may be native and, originate from non-cultivated areas

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…

Decision tree analysis to evaluate dry cow strategies under UK conditions.

PubMed

Berry, Elizabeth A; Hogeveen, Henk; Hillerton, J Eric

2004-11-01

Economic decisions on animal health strategies address the cost-benefit aspect along with animal welfare and public health concerns. Decision tree analysis at an individual cow level highlighted that there is little economic difference between the use of either dry cow antibiotic or an internal teat sealant in preventing a new intramammary infection in a cow free of infection in all quarters of the mammary gland at drying off. However, a potential net loss of over ł20 per cow might occur if the uninfected cow was left untreated. The only economically viable option, for a cow with one or more quarters infected at drying off, is antibiotic treatment, although a loss might still be incurred depending on the pathogen concerned and the cure rates achievable. There was a net loss for cows with quarters infected with Corynebacterium spp. at drying off, for both the teat sealant and untreated groups (ł22 and ł48, respectively) with only antibiotic-treated cows showing a gain.

Improving paediatric asthma care in Zambia

PubMed Central

Jumbe-Marsden, Emilia; Mateyo, Kondwelani; Senkwe, Mutale Nsakashalo; Sotomayor-Ruiz, Maria; Musuku, John; Soriano, Joan B; Ancochea, Julio; Fishman, Mark C

2015-01-01

Abstract Problem In 2008, the prevalence of paediatric asthma in Zambia was unknown and the national treatment guideline was outdated. Approach We created an international partnership between Zambian clinicians, the Zambian Government and a pharmaceutical company to address shortcomings in asthma treatment. We did two studies, one to estimate prevalence in the capital of Lusaka and one to assess attitudes and practices of patients. Based on the information obtained, we educated health workers and the public. The information from the studies was also used to modernize government policy for paediatric asthma management. Local setting The health-care system in Zambia is primarily focused on acute care delivery with a focus on infectious diseases. Comprehensive services for noncommunicable diseases are lacking. Asthma management relies on treatment of acute exacerbations instead of disease control. Relevant changes Seven percent of children surveyed had asthma (255/3911). Of the 120 patients interviewed, most (82/120, 68%) used oral short-acting β2-agonists for symptom control; almost half (59/120, 49%) did not think the symptoms were preventable and 43% (52/120) thought inhalers were addictive. These misconceptions informed broad-based educational programmes. We used a train-the-trainer model to educate health-care workers and ran public awareness campaigns. Access to inhalers was increased and the Zambian standard treatment guideline for paediatric asthma was revised to include steroid inhalers as a control treatment. Lessons learnt Joint activities were required to change paediatric asthma care in Zambia. Success will depend on local sustainability, and it may be necessary to shift resources to mirror the disease burden. PMID:26600616

A Novel Treatment Decision Tree and Literature Review of Retrograde Peri-Implantitis.

PubMed

Sarmast, Nima D; Wang, Howard H; Soldatos, Nikolaos K; Angelov, Nikola; Dorn, Samuel; Yukna, Raymond; Iacono, Vincent J

2016-12-01

Although retrograde peri-implantitis (RPI) is not a common sequela of dental implant surgery, its prevalence has been reported in the literature to be 0.26%. Incidence of RPI is reported to increase to 7.8% when teeth adjacent to the implant site have a previous history of root canal therapy, and it is correlated with distance between implant and adjacent tooth and/or with time from endodontic treatment of adjacent tooth to implant placement. Minimum 2 mm space between implant and adjacent tooth is needed to decrease incidence of apical RPI, with minimum 4 weeks between completion of endodontic treatment and actual implant placement. The purpose of this study is to compile all available treatment modalities and to provide a decision tree as a general guide for clinicians to aid in diagnosis and treatment of RPI. Literature search was performed for articles published in English on the topic of RPI. Articles selected were case reports with study populations ranging from 1 to 32 patients. Any case report or clinical trial that attempted to treat or rescue an implant diagnosed with RPI was included. Predominant diagnostic presentation of a lesion was presence of sinus tract at buccal or facial abscess of apical portion of implant, and subsequent periapical radiographs taken demonstrated a radiolucent lesion. On the basis of case reports analyzed, RPI was diagnosed between 1 week and 4 years after implant placement. Twelve of 20 studies reported that RPI lesions were diagnosed within 6 months after implant placement. A step-by-step decision tree is provided to allow clinicians to triage and properly manage cases of RPI on the basis of recommendations and successful treatments provided in analyzed case reports. It is divided between symptomatic and asymptomatic implants and adjacent teeth with vital and necrotic pulps. Most common etiology of apical RPI is endodontic infection from neighboring teeth, which was diagnosed within 6 months after implant placement. Most

The Role of Open and Distance Learning in the Implementation of the Right to Education in Zambia

ERIC Educational Resources Information Center

Siaciwena, Richard; Lubinda, Foster

2008-01-01

As a member of the United Nations, Zambia is committed to the observance of human rights enshrined in the Universal Declaration of Human Rights of 1948. This is evidenced, among others, by the fact that Zambia is a signatory to the Convention on the Rights of the Child and the African Charter on the Rights and Welfare of the Child. Zambia has a…

Insecticide-treated nets mass distribution campaign: benefits and lessons in Zambia.

PubMed

Masaninga, Freddie; Mukumbuta, Nawa; Ndhlovu, Ketty; Hamainza, Busiku; Wamulume, Pauline; Chanda, Emmanuel; Banda, John; Mwanza-Ingwe, Mercy; Miller, John M; Ameneshewa, Birkinesh; Mnzava, Abraham; Kawesha-Chizema, Elizabeth

2018-04-24

Zambia was an early adopter of insecticide-treated nets strategy in 2001, and policy for mass distribution with long-lasting insecticidal nets (LLINs) in 2005. Since then, the country has implemented mass distribution supplemented with routine delivery through antenatal care and under five clinics in health facilities. The national targets of universal (100%) coverage and 80% utilization of LLINs have not been attained. Free mass LLIN distribution campaign in Zambia offers important lessons to inform future campaigns in the African region. This study reviewed LLIN free mass distribution campaign information derived from Zambia's national and World Health Organization Global Malaria Programme annual reports and strategic plans published between 2001 and 2016. In 2014, a nationwide mass distribution campaign in Zambia delivered all the 6.0 million LLINs in 6 out of 10 provinces in 4 months between June and September before the onset of the rainy season. Compared with 235,800 LLINs and 2.9 million LLINs distributed on a rolling basis in 2008 and 2013, respectively, the 2014 mass campaign, which distributed 6 million LLINs represented the largest one-time-nationwide LLIN distribution in Zambia. The province (Luapula) with highest malaria transmission, mostly with rural settings recorded 98-100% sleeping spaces in homes covered with LLINs. The percentage of households owning at least 1 LLIN increased from 50.9% in 2006 to 77.7% in 2015. The 2014 mass campaign involved a coordinated response with substantial investments into macro (central) and micro (district) level planning, capacity building, tracking and logistics management supported by a new non-health sector partnership landscape. Coordination of LLIN distribution and logistics benefited from the mobile phone technology to transmit "real time" data on commodity tracking that facilitated timely delivery to districts. Free mass distribution of LLINs policy was adopted in 2005 in Zambia. Consistently implemented

Application of a hybrid association rules/decision tree model for drought monitoring

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Molajou, Amir

2017-12-01

The previous researches have shown that the incorporation of the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) into hydro-climatic models could provide important predictive information about hydro-climatic variability. In this paper, the hybrid application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between drought of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. Two major steps of the proposed model were the classification of de-trend SST data and selecting the most effective groups and extracting hidden information involved in the data. The techniques of decision tree which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and Heidke Skill Score (HSS) measures were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast drought and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and drought of Tabriz and Kermanshah synoptic stations so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 70 and 80% respectively for Tabriz and Kermanshah synoptic stations.

Patterns of Rift Valley fever activity in Zambia.

PubMed Central

Davies, F. G.; Kilelu, E.; Linthicum, K. J.; Pegram, R. G.

1992-01-01

An hypothesis that there was an annual emergence of Rift Valley fever virus in Zambia, during or after the seasonal rains, was examined with the aid of sentinel cattle. Serum samples taken during 1974 and 1978 showed evidence of epizootic Rift Valley fever in Zambia, with more than 80% positive. A sentinel herd exposed from 1982 to 1986 showed that some Rift Valley fever occurred each year. This was usually at a low level, with 3-8% of the susceptible cattle seroconverting. In 1985-6 more than 20% of the animals seroconverted, and this greater activity was associated with vegetational changes--which could be detected by remote-sensing satellite imagery--which have also been associated with greater virus activity in Kenya. PMID:1547835

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

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

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.

OUTLINE OF VOCATIONAL TRAINING IN ZAMBIA.

ERIC Educational Resources Information Center

Australian Dept. of Labour and National Service, Perth.

THE 1963 POPULATION OF ZAMBIA WAS APPROXIMATELY 3.5 MILLION. THE 8-YEAR PRIMARY EDUCATION PROGRAM IS FOLLOWED BY SECONDARY, SECONDARY TECHNICAL, AND TRADE SCHOOL OPTIONS. THERE IS AN INCREASE IN ADULT EDUCATION AT THE PRIMARY AND SECONDARY LEVELS. CRAFT AND TECHNICIAN LEVEL PROGRAMS ARE CONDUCTED AT NORTHERN TECHNICAL COLLEGE AND ITS ANCILLARY…

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.

Aflatoxin contamination of groundnut and maize in Zambia: observed and potential concentrations.

PubMed

Kachapulula, P W; Akello, J; Bandyopadhyay, R; Cotty, P J

2017-06-01

The aims of the study were to quantify aflatoxins, the potent carcinogens associated with stunting and immune suppression, in maize and groundnut across Zambia's three agroecologies and to determine the vulnerability to aflatoxin increases after purchase. Aflatoxin concentrations were determined for 334 maize and groundnut samples from 27 districts using lateral-flow immunochromatography. Seventeen per cent of crops from markets contained aflatoxin concentrations above allowable levels in Zambia (10 μg kg -1 ). Proportions of crops unsafe for human consumption differed significantly (P < 0·001) among agroecologies with more contamination (38%) in the warmest (Agroecology I) and the least (8%) in cool, wet Agroecology III. Aflatoxin in groundnut (39 μg kg -1 ) and maize (16 μg kg -1 ) differed (P = 0·032). Poor storage (31°C, 100% RH, 1 week) increased aflatoxin in safe crops by over 1000-fold in both maize and groundnut. The L morphotype of Aspergillus flavus was negatively correlated with postharvest increases in groundnut. Aflatoxins are common in Zambia's food staples with proportions of unsafe crops dependent on agroecology. Fungal community structure influences contamination suggesting Zambia would benefit from biocontrol with atoxigenic A. flavus. Aflatoxin contamination across the three agroecologies of Zambia is detailed and the case for aflatoxin management with atoxigenic biocontrol agents provided. The first method for evaluating the potential for aflatoxin increase after purchase is presented. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Journal of Applied Microbiology published by John Wiley & Sons Ltd on behalf of The Society for Applied Microbiology.

AIDS education for a low literate audience in Zambia.

PubMed

Msimuko, A K

1988-04-01

A workshop funded by the USA Program for Appropriate Technology in Health (PATH) was an effort by Zambia toward prevention and control of AIDS. The lack of educational materials about AIDS for a low-literate audience was the major problem addressed by the workshop. Other problems include the lack of collaborative effort in the development of materials on AIDS, and the lack of skills needed in the development of such materials in Zambia. 1 of the objectives of the workshop was to launch the Planned Parenthood Association of Zambia's (PPAZ) materials development project. The scope of this project includes the production of educational materials on AIDS for low-literate audiences and a counseling handbook for family planning workers. Print materials should be simply written, using words, idioms, and graphics that are familiar to the target audience. Other workshop objectives included the establishment of collaborative relationships between organizations involved in existing AIDS educational activities in Zambia, and the development of practical skills needed to produce print materials. Education was identified as the most important strategy for the prevention and control of AIDS, and PPAZ should be the executing agency of the print materials project. Audience research, using focus group techniques, focus group discussions, behavioral messages, and pretesting of messages, should be the most effective means of reaching targeted audiences. PPAZ is contracted by PATH to begin development of educational materials, and 2 committees have formed to implement the project and to establish interagency collaboration. Audience research was begun between January and March of 1988, focusing on people's beliefs, practices, and ideas about AIDS. The final phase of the project will be the printing, distribution, and use of the AIDS materials and the training of family planning field workers in the proper use of these materials.

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.

Co-circulation of multiple genotypes of African swine fever viruses among domestic pigs in Zambia (2013-2015).

PubMed

Simulundu, E; Chambaro, H M; Sinkala, Y; Kajihara, M; Ogawa, H; Mori, A; Ndebe, J; Dautu, G; Mataa, L; Lubaba, C H; Simuntala, C; Fandamu, P; Simuunza, M; Pandey, G S; Samui, K L; Misinzo, G; Takada, A; Mweene, A S

2018-02-01

During 2013-2015, several and severe outbreaks of African swine fever (ASF) affected domestic pigs in six provinces of Zambia. Genetic characterization of ASF viruses (ASFVs) using standardized genotyping procedures revealed that genotypes I, II and XIV were associated with these outbreaks. Molecular and epidemiological data suggest that genotype II ASFV (Georgia 2007/1-like) detected in Northern Province of Zambia may have been introduced from neighbouring Tanzania. Also, a genotype II virus detected in Eastern Province of Zambia showed a p54 phylogenetic relationship that was inconsistent with that of p72, underscoring the genetic variability of ASFVs. While it appears genotype II viruses detected in Zambia arose from a domestic pig cycle, genotypes I and XIV possibly emerged from a sylvatic cycle. Overall, this study demonstrates the co-circulation of multiple genotypes of ASFVs, involvement of both the sylvatic and domestic pig cycle in ASF outbreaks in Zambia and possible trans-boundary spread of the disease in south-eastern Africa. Indeed, while there is need for regional or international concerted efforts in the control of ASF, understanding pig marketing practices, pig population dynamics, pig housing and rearing systems and community engagement will be important considerations when designing future prevention and control strategies of this disease in Zambia. © 2017 Blackwell Verlag GmbH.

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...

Decision trees for the analysis of genes involved in Alzheimer's disease pathology.

PubMed

Mestizo Gutiérrez, Sonia L; Herrera Rivero, Marisol; Cruz Ramírez, Nicandro; Hernández, Elena; Aranda-Abreu, Gonzalo E

2014-09-21

Alzheimer's disease (AD) is characterized by a gradual loss of memory, orientation, judgement and language. There is still no cure for this disorder. AD pathogenesis remains fairly unknown and its underlying molecular mechanisms are not yet fully understood. Several studies have shown that the abnormal accumulation of beta-amyloid and tau proteins occurs 10 to 20 years before the onset of symptoms of the disease, so it is extremely important to identify changes in the brain before the first symptoms. We used decision trees to classify 31 individuals (9 healthy controls and 22 AD patients in three different stages of disease) according to the expression of 69 genes previously reported in a meta-analysis, plus the expression levels of APP, APOE, BACE1, NCSTN, PSEN1, PSEN2 and MAPT. We also included in our analysis the MMSE (Mini-Mental State Examination) scores and number of NFT (neurofibrillary tangles). Results allowed us to generate a model of classification values for different AD stages of severity, according to MMSE scores, and achieve the identification of the expression level of protein tau that may possibly determine the onset (incipient stage) of AD. We used decision trees to model the different stages of AD (severe, moderate, incipient and control) based on the meta-analysis of gene expression levels plus MMSE and NFT scores. Both classifiers reported the variable MMSE as most informative, however it we were found that the protein tau also an important role in the onset of AD. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

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%.

Factors influencing modes of transport and travel time for obstetric care: a mixed methods study in Zambia and Uganda.

PubMed

Sacks, Emma; Vail, Daniel; Austin-Evelyn, Katherine; Greeson, Dana; Atuyambe, Lynn M; Macwan'gi, Mubiana; Kruk, Margaret E; Grépin, Karen A

2016-04-01

Transportation is an important barrier to accessing obstetric care for many pregnant and postpartum women in low-resource settings, particularly in rural areas. However, little is known about how pregnant women travel to health facilities in these settings. We conducted 1633 exit surveys with women who had a recent facility delivery and 48 focus group discussions with women who had either a home or a facility birth in the past year in eight districts in Uganda and Zambia. Quantitative data were analysed using univariate statistics, and qualitative data were analysed using thematic content analysis techniques. On average, women spent 62-68 min travelling to a clinic for delivery. Very different patterns in modes of transport were observed in the two countries: 91% of Ugandan women employed motorized forms of transportation, while only 57% of women in Zambia did. Motorcycle taxis were the most commonly used in Uganda, while cars, trucks and taxis were the most commonly used mode of transportation in Zambia. Lower-income women were less likely to use motorized modes of transportation: in Zambia, women in the poorest quintile took 94 min to travel to a health facility, compared with 34 for the wealthiest quintile; this difference between quintiles was ∼50 min in Uganda. Focus group discussions confirmed that transport is a major challenge due to a number of factors we categorized as the 'three A's:' affordability, accessibility and adequacy of transport options. Women reported that all of these factors had influenced their decision not to deliver in a health facility. The two countries had markedly different patterns of transportation for obstetric care, and modes of transport and travel times varied dramatically by wealth quintile, which policymakers need to take into account when designing obstetric transport interventions. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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

Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection.

PubMed

Verhofstede, Chris; Fransen, Katrien; Van Den Heuvel, Annelies; Van Laethem, Kristel; Ruelle, Jean; Vancutsem, Ellen; Stoffels, Karolien; Van den Wijngaert, Sigi; Delforge, Marie-Luce; Vaira, Dolores; Hebberecht, Laura; Schauvliege, Marlies; Mortier, Virginie; Dauwe, Kenny; Callens, Steven

2017-11-29

There is today no gold standard method to accurately define the time passed since infection at HIV diagnosis. Infection timing and incidence measurement is however essential to better monitor the dynamics of local epidemics and the effect of prevention initiatives. Three methods for infection timing were evaluated using 237 serial samples from documented seroconversions and 566 cross sectional samples from newly diagnosed patients: identification of antibodies against the HIV p31 protein in INNO-LIA, SediaTM BED CEIA and SediaTM LAg-Avidity EIA. A multi-assay decision tree for infection timing was developed. Clear differences in recency window between BED CEIA, LAg-Avidity EIA and p31 antibody presence were observed with a switch from recent to long term infection a median of 169.5, 108.0 and 64.5 days after collection of the pre-seroconversion sample respectively. BED showed high reliability for identification of long term infections while LAg-Avidity is highly accurate for identification of recent infections. Using BED as initial assay to identify the long term infections and LAg-Avidity as a confirmatory assay for those classified as recent infection by BED, explores the strengths of both while reduces the workload. The short recency window of p31 antibodies allows to discriminate very early from early infections based on this marker. BED recent infection results not confirmed by LAg-Avidity are considered to reflect a period more distant from the infection time. False recency predictions in this group can be minimized by elimination of patients with a CD4 count of less than 100 cells/mm3 or without no p31 antibodies. For 566 cross sectional sample the outcome of the decision tree confirmed the infection timing based on the results of all 3 markers but reduced the overall cost from 13.2 USD to 5.2 USD per sample. A step-wise multi assay decision tree allows accurate timing of the HIV infection at diagnosis at affordable effort and cost and can be an important

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…

The contribution of trees outside forests to national tree biomass and carbon stocks--a comparative study across three continents.

PubMed

Schnell, Sebastian; Altrell, Dan; Ståhl, Göran; Kleinn, Christoph

2015-01-01

In contrast to forest trees, trees outside forests (TOF) often are not included in the national monitoring of tree resources. Consequently, data about this particular resource is rare, and available information is typically fragmented across the different institutions and stakeholders that deal with one or more of the various TOF types. Thus, even if information is available, it is difficult to aggregate data into overall national statistics. However, the National Forest Monitoring and Assessment (NFMA) programme of FAO offers a unique possibility to study TOF resources because TOF are integrated by default into the NFMA inventory design. We have analysed NFMA data from 11 countries across three continents. For six countries, we found that more than 10% of the national above-ground tree biomass was actually accumulated outside forests. The highest value (73%) was observed for Bangladesh (total forest cover 8.1%, average biomass per hectare in forest 33.4 t ha(-1)) and the lowest (3%) was observed for Zambia (total forest cover 63.9%, average biomass per hectare in forest 32 t ha(-1)). Average TOF biomass stocks were estimated to be smaller than 10 t ha(-1). However, given the large extent of non-forest areas, these stocks sum up to considerable quantities in many countries. There are good reasons to overcome sectoral boundaries and to extend national forest monitoring programmes on a more systematic basis that includes TOF. Such an approach, for example, would generate a more complete picture of the national tree biomass. In the context of climate change mitigation and adaptation, international climate mitigation programmes (e.g. Clean Development Mechanism and Reduced Emission from Deforestation and Degradation) focus on forest trees without considering the impact of TOF, a consideration this study finds crucial if accurate measurements of national tree biomass and carbon pools are required.

Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

NASA Astrophysics Data System (ADS)

Höhle, J.

2014-09-01

A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

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.

Personal and Environmental Predictors of the Intention to Use Maternal Healthcare Services in Kalomo, Zambia

ERIC Educational Resources Information Center

Sialubanje, Cephas; Massar, Karlijn; Hamer, Davidson H.; Ruiter, Robert A. C.

2014-01-01

Low maternal healthcare service utilization contributes to poor maternal and new born health outcomes in rural Zambia. The purpose of this study was to identify important factors influencing women's intention to use these services in Kalomo, Zambia. An interviewer-administered questionnaire was used to collect data from 1007 women of reproductive…

Fire management assessment of Eastern Province, Zambia

Treesearch

L. T. Hollingsworth; D. Johnson; G. Sikaundi; S. Siame

2015-01-01

The mission that produced this assessment was prompted by requests from Forestry Department personnel in Zambia to the United States Agency for International Development (USAID) for formal fire management training. USAID contacted the United States Forest Service's (USFS) International Programs (IP) with the training request. Together, USFS, USAID, and Zambian...

Reasons for home delivery and use of traditional birth attendants in rural Zambia: a qualitative study.

PubMed

Sialubanje, Cephas; Massar, Karlijn; Hamer, Davidson H; Ruiter, Robert A C

2015-09-11

Despite the policy change stopping traditional birth attendants (TBAs) from conducting deliveries at home and encouraging all women to give birth at the clinic under skilled care, many women still give birth at home and TBAs are essential providers of obstetric care in rural Zambia. The main reasons for pregnant women's preference for TBAs are not well understood. This qualitative study aimed to identify reasons motivating women to giving birth at home and seek the help of TBAs. This knowledge is important for the design of public health interventions focusing on promoting facility-based skilled birth attendance in Zambia. We conducted ten focus group discussions (n = 100) with women of reproductive age (15-45 years) in five health centre catchment areas with the lowest institutional delivery rates in the district. In addition, a total of 30 in-depth interviews were conducted comprising 5 TBAs, 4 headmen, 4 husbands, 4 mothers, 4 neighbourhood health committee (NHC) members, 4 community health workers (CHWs) and 5 nurses. Perspectives on TBAs, the decision-making process regarding home delivery and use of TBAs, and reasons for preference of TBAs and their services were explored. Our findings show that women's lack of decision- making autonomy regarding child birth, dependence on the husband and other family members for the final decision, and various physical and socioeconomic barriers including long distances, lack of money for transport and the requirement to bring baby clothes and food while staying at the clinic, prevented them from delivering at a clinic. In addition, socio-cultural norms regarding childbirth, negative attitude towards the quality of services provided at the clinic, made most women deliver at home. Moreover, most women had a positive attitude towards TBAs and perceived them to be respectful, skilled, friendly, trustworthy, and available when they needed them. Our findings suggest a need to empower women with decision-making skills

Non-prescription sale and dispensing of antibiotics in community pharmacies in Zambia.

PubMed

Kalungia, Aubrey Chichonyi; Burger, Johanita; Godman, Brian; Costa, Juliana de Oliveira; Simuwelu, Chimwemwe

2016-12-01

In Zambia, antibiotics are categorized as prescription-only medicines. Antibiotics dispensed without a prescription pose a public health threat, which is a concern. Consequently, the aim is to ascertain the extent of non-prescription sales and dispensing of antibiotics in community pharmacies in Zambia. The practice of non-prescription sale and dispensing were assessed in 73 randomly selected community retail pharmacies, using a structured interviewer-administered questionnaire with simulated case scenarios. Majority (97%) stated that clients frequently requested non-prescribed antibiotics. Interviewees usually asked clients' indications (94%), counselled on dosing (96%) and suggested changes to antibiotic choices (97%). All (100%) dispensed non-prescribed antibiotics. Commonly dispensed antibiotics included amoxicillin (52%), cotrimoxazole (25%) and metronidazole (23%). Non-prescription sale and dispensing of antibiotics was significantly associated with interviewees' professional qualification in four out of five simulations. Non-prescription sale and dispensing of antibiotics is widespread in Zambia. Concerted public and professional interventions are needed coupled with stronger regulatory enforcement to reduce this.

Lusaka, Zambia, during SAFARI-2000: Convergence of local and imported ozone pollution

NASA Astrophysics Data System (ADS)

Thompson, Anne M.; Witte, Jacquelyn C.; Freiman, M. Tal; Phahlane, N. Agnes; Coetzee, Gert J. R.

2002-10-01

In August and September, throughout south central Africa, seasonal clearing of dry vegetation and other fire-related activities lead to intense smoke haze and ozone formation. The first ozone soundings in the heart of the southern African burning region were taken at Lusaka, Zambia (15.5S, 28E) in early September 2000. Maximum surface ozone was over 90 ppbv and column tropospheric ozone exceeded 50 DU. These values are higher than concurrent measurements over Nairobi (1S, 38E) and Irene (25S, 28E, near Pretoria). At least 30% of Lusaka surface ozone appears to be from local sources. A layer at 800-500 hPa has ozone >120 ppbv and originates from trans-boundary recirculation. Starting out over Zambia, Angola, and Namibia, ozone-rich air travels east to the Indian Ocean, before heading back toward Mozambique, Zimbabwe and Zambia. Thus, Lusaka collects local and imported pollution, consistent with its location within the southern African gyre.

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

Inequalities in public health care delivery in Zambia

PubMed Central

2014-01-01

Background Access to adequate health services that is of acceptable quality is important in the move towards universal health coverage. However, previous studies have revealed inequities in health care utilisation in the favour of the rich. Further, those with the greatest need for health services are not getting a fair share. In Zambia, though equity in access is extolled in government documents, there is evidence suggesting that those needing health services are not receiving their fair share. This study seeks therefore, to assess if socioeconomic related inequalities/inequities in public health service utilisation in Zambia still persist. Methods The 2010 nationally representative Zambia Living Conditions and Monitoring Survey data are used. Inequality is assessed using concentration curves and concentrations indices while inequity is assessed using a horizontal equity index: an index of inequity across socioeconomic status groups, based on standardizing health service utilisation for health care need. Public health services considered include public health post visits, public clinic visits, public hospital visits and total public facility visits. Results There is evidence of pro-poor inequality in public primary health care utilisation but a pro-rich inequality in hospital visits. The concentration indices for public health post visits and public clinic visits are −0.28 and −0.09 respectively while that of public hospitals is 0.06. After controlling for need, the pro-poor distribution is maintained at primary facilities and with a pro-rich distribution at hospitals. The horizontal equity indices for health post and clinic are estimated at −0.23 and −0.04 respectively while that of public hospitals is estimated at 0.11. A pro-rich inequity is observed when all the public facilities are combined (horizontal equity index = 0.01) though statistically insignificant. Conclusion The results of the paper point to areas of focus in ensuring equitable access

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…

Assessing income redistributive effect of health financing in Zambia.

PubMed

Mulenga, Arnold; Ataguba, John Ele-Ojo

2017-09-01

Ensuring an equitable health financing system is a major concern particularly in many developing countries. Internationally, there is a strong debate to move away from excessive reliance on direct out-of-pocket (OOP) spending towards a system that incorporates a greater element of risk pooling and thus affords greater protection for the poor. This is a major focus of the move towards universal health coverage (UHC). Currently, Zambia with high levels of poverty and income inequality is implementing health sector reforms for UHC through a social health insurance scheme. However, the way to identify the health financing mechanisms that are best suited to achieving this goal is to conduct empirical analysis and consider international evidence on funding universal health systems. This study assesses, for the first time, the progressivity of health financing and how it impacts on income inequality in Zambia. Three broad health financing mechanisms (general tax, a health levy and OOP spending) were considered. Data come from the 2010 nationally representative Zambian Living Conditions and Monitoring Survey with a sample size of 19,397 households. Applying standard methodologies, the findings show that total health financing in Zambia is progressive. It also leads to a statistically significant reduction in income inequality (i.e. a pro-poor redistributive effect estimated at 0.0110 (p < 0.01)). Similar significant pro-poor redistribution was reported for general taxes (0.0101 (p < 0.01)) and a health levy (0.0002 (p < 0.01)). However, the redistributive effect was not significant for OOP spending (0.0006). These results further imply that health financing redistributes income from the rich to the poor with a greater potential via general taxes. This points to areas where government policy may focus in attempting to reduce the high level of income inequality and to improve equity in health financing towards UHC in Zambia. Copyright © 2017 Elsevier Ltd. All rights

Health worker shortages in Zambia: an assessment of government responses.

PubMed

Gow, Jeff; George, Gavin; Mutinta, Given; Mwamba, Sylvia; Ingombe, Lutungu

2011-11-01

A dire health worker shortage in Zambia's national health programs is adversely impacting the quantity and quality of health care and posing a serious barrier to achieving Millennium Development Goals to improve population health. In 2005, Zambia's Ministry of Health developed a 10-year strategic plan for human resources for health to address the crisis through improved training, hiring, and retention. The plan has neither arrested nor reduced the shortage. We review the causes of the shortage, present results from a health worker survey showing that safe work conditions, manageable workloads, and career advancement opportunities matter more to respondents than financial compensation. We comment on the adequacy of government efforts to address the health worker shortage.

Cholera Epidemic - Lusaka, Zambia, October 2017-May 2018.

PubMed

Sinyange, Nyambe; Brunkard, Joan M; Kapata, Nathan; Mazaba, Mazyanga Lucy; Musonda, Kunda G; Hamoonga, Raymond; Kapina, Muzala; Kapaya, Fred; Mutale, Lwito; Kateule, Ernest; Nanzaluka, Francis; Zulu, James; Musyani, Chileshe Lukwesa; Winstead, Alison V; Davis, William W; N'cho, Hammad S; Mulambya, Nelia L; Sakubita, Patrick; Chewe, Orbie; Nyimbili, Sulani; Onwuekwe, Ezinne V C; Adrien, Nedghie; Blackstock, Anna J; Brown, Travis W; Derado, Gordana; Garrett, Nancy; Kim, Sunkyung; Hubbard, Sydney; Kahler, Amy M; Malambo, Warren; Mintz, Eric; Murphy, Jennifer; Narra, Rupa; Rao, Gouthami G; Riggs, Margaret A; Weber, Nicole; Yard, Ellen; Zyambo, Khozya D; Bakyaita, Nathan; Monze, Namani; Malama, Kennedy; Mulwanda, Jabbin; Mukonka, Victor M

2018-05-18

On October 6, 2017, an outbreak of cholera was declared in Zambia after laboratory confirmation of Vibrio cholerae O1, biotype El Tor, serotype Ogawa, from stool specimens from two patients with acute watery diarrhea. The two patients had gone to a clinic in Lusaka, the capital city, on October 4. Cholera cases increased rapidly, from several hundred cases in early December 2017 to approximately 2,000 by early January 2018 (Figure). In collaboration with partners, the Zambia Ministry of Health (MoH) launched a multifaceted public health response that included increased chlorination of the Lusaka municipal water supply, provision of emergency water supplies, water quality monitoring and testing, enhanced surveillance, epidemiologic investigations, a cholera vaccination campaign, aggressive case management and health care worker training, and laboratory testing of clinical samples. In late December 2017, a number of water-related preventive actions were initiated, including increasing chlorine levels throughout the city's water distribution system and placing emergency tanks of chlorinated water in the most affected neighborhoods; cholera cases declined sharply in January 2018. During January 10-February 14, 2018, approximately 2 million doses of oral cholera vaccine were administered to Lusaka residents aged ≥1 year. However, in mid-March, heavy flooding and widespread water shortages occurred, leading to a resurgence of cholera. As of May 12, 2018, the outbreak had affected seven of the 10 provinces in Zambia, with 5,905 suspected cases and a case fatality rate (CFR) of 1.9%. Among the suspected cases, 5,414 (91.7%), including 98 deaths (CFR = 1.8%), occurred in Lusaka residents.

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…

Examining the Role of Couples' Characteristics in Contraceptive use in Nigeria and Zambia.

PubMed

Ntoimo, Lorretta Favour C; Chirwa-Banda, Pamela

2017-12-01

Relationship-related characteristics influence diverse health and demographic outcomes. This study examined the role of couples' characteristics in contraceptive use. Data were obtained from 2013 Nigeria and 2013-14 Zambia Demographic and Health Surveys. The study population consisted of couples in monogamous union (married or living together) who had at least one live birth and the wife was not pregnant at the time of the survey. Prevalence of contraceptive use among couples in Nigeria was 27% and 63% in Zambia. Couples' educational attainment, religious affiliation, the frequency of listening to the radio, reported number of children, fertility preference, region of residence and household wealth index were significant predictors of contraceptive use among couples in Nigeria and Zambia. Given the significant role of couples' characteristics in the uptake of contraceptives, there is the need to encourage interventions that target couples, particularly those of poor socioeconomic status.

Mapping Disparities in Access to Safe, Timely, and Essential Surgical Care in Zambia.

PubMed

Esquivel, Micaela M; Uribe-Leitz, Tarsicio; Makasa, Emmanuel; Lishimpi, Kennedy; Mwaba, Peter; Bowman, Kendra; Weiser, Thomas G

2016-11-01

Surgical care is widely unavailable in developing countries; advocates recommend that countries evaluate and report on access to surgical care to improve availability and aid health planners in decision making. To analyze the infrastructure, capacity, and availability of surgical care in Zambia to inform health policy priorities. In this observational study, all hospitals providing surgical care were identified in cooperation with the Zambian Ministry of Health. On-site data collection was conducted from February 1 through August 30, 2011, with an adapted World Health Organization Global Initiative for Emergency and Essential Surgical Care survey. Data collection at each facility included interviews with hospital personnel and assessment of material resources. Data were geocoded and analyzed in a data visualization platform from March 1 to December 1, 2015. We analyzed time and distance to surgical services, as well as the proportion of the population living within 2 hours from a facility providing surgical care. Surgical capacity, supplies, human resources, and infrastructure at each surgical facility, as well as the population living within 2 hours from a hospital providing surgical care. Data were collected from all 103 surgical facilities identified as providing surgical care. When including all surgical facilities (regardless of human resources and supplies), 14.9% of the population (2 166 460 of 14 500 000 people) lived more than 2 hours from surgical care. However, only 17 hospitals (16.5%) met the World Health Organization minimum standards of surgical safety; when limiting the analysis to these hospitals, 65.9% of the population (9 552 780 people) lived in an area that was more than 2 hours from a surgical facility. Geographic analysis of emergency and essential surgical care, defined as access to trauma care, obstetric care, and care of common abdominal emergencies, found that 80.7% of the population (11 704 700 people) lived in an area

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...

Priorities for Antiretroviral Therapy Research in Sub-Saharan Africa: A 2002 Consensus Conference in Zambia

PubMed Central

Zulu, Isaac; Schuman, Paula; Musonda, Rosemary; Chomba, Elwyn; Mwinga, Kasonde; Sinkala, Moses; Chisembele, Maureen; Mwaba, Peter; Kasonde, Dorothy; Vermund, Sten H.

2009-01-01

Background A consensus conference was held to discuss priorities for antiretroviral therapy (ART) research in Zambia, one of the world’s most heavily HIV-afflicted nations. Zambia, like other resource-limited settings, has increasing access to highly active antiretroviral therapy (HAART) because of declining drug costs, use of government-purchased generic medications, and increased global donations. For sustained delivery of care with HAART in a resource-constrained medical and public health context, operational research is required and clinical trials are desirable. The priority areas for research are most relevant today given the increasing availability of HAART. Methods A conference was held in Lusaka, Zambia, in January 2002 to discuss priority areas for ART research in Zambia, with participants drawn from a broad cross section of Zambian society. State-of-the-art reviews and 6 intensive small group discussions helped to formulate a suggested research agenda. Results Conference participants believed that the most urgent research priorities were to assess how therapeutic resources could be applied for the greatest overall benefit and to minimize the impact of nonadherence and viral resistance. Identified research priorities were as follows: To determine when to initiate HAART in relation to CD4+ cell count To assess whether HIV/AIDS can be managed well without the use of costly frequent viral load measurements and CD4+ cell count monitoring To assess whether HIV/AIDS can be managed in the same fashion in patients coinfected with opportunistic infections such as tuberculosis and HIV-related chronic diarrhea, taking into consideration complications that may occur in tuberculosis such as immune reconstitution syndrome and medication malabsorption in the presence of diarrhea To carefully assess and characterize toxicities, adverse effects, and viral resistance patterns in Zambia, including studies of mothers exposed to prepartum single-dose nevirapine To conduct

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.

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

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.

Cost of abortions in Zambia: A comparison of safe abortion and post abortion care.

PubMed

Parmar, Divya; Leone, Tiziana; Coast, Ernestina; Murray, Susan Fairley; Hukin, Eleanor; Vwalika, Bellington

2017-02-01

Unsafe abortion is a significant but preventable cause of maternal mortality. Although induced abortion has been legal in Zambia since 1972, many women still face logistical, financial, social, and legal obstacles to access safe abortion services, and undergo unsafe abortion instead. This study provides the first estimates of costs of post abortion care (PAC) after an unsafe abortion and the cost of safe abortion in Zambia. In the absence of routinely collected data on abortions, we used multiple data sources: key informant interviews, medical records and hospital logbooks. We estimated the costs of providing safe abortion and PAC services at the University Teaching Hospital, Lusaka and then projected these costs to generate indicative cost estimates for Zambia. Due to unavailability of data on the actual number of safe abortions and PAC cases in Zambia, we used estimates from previous studies and from other similar countries, and checked the robustness of our estimates with sensitivity analyses. We found that PAC following an unsafe abortion can cost 2.5 times more than safe abortion care. The Zambian health system could save as much as US$0.4 million annually if those women currently treated for an unsafe abortion instead had a safe abortion.

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.

Below and above-ground carbon distribution along a rainfall gradient. A case of the Zambezi teak forests, Zambia

NASA Astrophysics Data System (ADS)

Ngoma, Justine; Moors, Eddy; Kruijt, Bart; Speer, James H.; Vinya, Royd; Chidumayo, Emmanuel N.; Leemans, Rik

2018-02-01

Understanding carbon (C) stocks or biomass in forests is important to examine how forests mitigate climate change. To estimate biomass in stems, branches and roots takes intensive fieldwork to uproot, cut and weigh the mass of each component. Different models or equations are also required. Our research focussed on the dry tropical Zambezi teak forests and we studied their structure at three sites following a rainfall gradient in Zambia. We sampled 3558 trees at 42 plots covering a combined area of 15ha. Using data from destructive tree samples, we developed mixed-species biomass models to estimate above ground biomass for small (<5 cm diameter at breast height (DBH, 1.3 m above-ground)) and large (≥5 cm DBH) trees involving 90 and 104 trees respectively, that belonged to 12 species. A below-ground biomass model was developed from seven trees of three species (16-44 cm DBH) whose complete root systems were excavated. Three stump models were also derived from these uprooted trees. Finally, we determined the C fractions from 194 trees that belonged to 12 species. The analysis revealed that DBH was the only predictor that significantly correlated to both above-ground and below-ground biomass. We found a mean root-to-shoot ratio of 0.38:0.62. The C fraction in leaves ranged from 39% to 42%, while it varied between 41% and 46% in wood. The C fraction was highest at the Kabompo site that received the highest rainfall, and lowest at the intermediate Namwala site. The C stocks varied between 15 and 36 ton C ha-1 and these stocks where highest at the wetter Kabompo site and lowest at the drier Sesheke site. Our results indicate that the projected future rainfall decrease for southern Africa, will likely reduce the C storage potential of the Zambezi teak forests, thereby adversely affecting their mitigating role in climate change.

Trends of selected cattle diseases in eastern Zambia between 1988 and 2008.

PubMed

Mubamba, Chrisborn; Sitali, Joseph; Gummow, Bruce

2011-09-01

Livestock diseases have long been a challenge to livestock production and public health in sub-Saharan Africa and Zambia in particular. The Eastern Province of Zambia is one area in Zambia that is not spared by this challenge. Among various livestock diseases affecting cattle in this region, the most prominent are East Coast Fever (ECF) and African Animal Trypanasomiasis (AAT). Since little has been published on the epidemiological trends of these diseases in eastern Zambia, a retrospective epidemiological study was carried out using reports that were submitted to the provincial veterinary office over the past 20 years. This paper assists in evaluating the impact of some of these aid programmes. Data was analysed using Excel(©), SPSS(®), Epi Info(©), and Epi Map(©) software. Apparent prevalence of AAT in cattle had decreased in the study period from estimates as high as 50% in Katete and Petauke district in 1990 and 1992 respectively to just below 3% (Petauke and Katete) in 2008, thereby, reducing the provincial apparent prevalence from 20% in 1992 to just below 3% in 2008. AAT apparent prevalence dropped from estimates as high as 17% in Chadiza district and 6% in Chipata district in 1990 to just below 1% in 2008 thereby reducing the provincial mean prevalence of East Coast Fever from 6% (1990) to 1% (2008). The inclusion of donor assistance in disease control programmes for both AAT and ECF appeared to have a significant impact on the prevalence of both diseases. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Observation of the total solar eclipse on 21 June 2001 in Zambia

NASA Astrophysics Data System (ADS)

Takahashi, Noritsugu; Yumoto, Kiyohumi; Ichimoto, Kiyoshi

2002-04-01

On 21 June 2001, path of totality in Angola, Zambia, Zimbabwe, Mozambique, and Madagascar in Africa. The Japan Scientific Observation Team, consisting primarily of the members of the Solar Eclipse Subcommittee of the Committee for International Collaboration in Astronomy of the Science Council of JAPAN, visited Lusaka in Zambia to observe the total solar eclipse. Blessed with fine weather, the observation was successful. The outline of the influence of solar eclipse on the terrestrial magnetism, polarization of the flash spectrum, and other observation data, as well as the way educational activities were carried out, are reported.

Application of Decision Tree in the Prediction of Periventricular Leukomalacia (PVL) Occurrence in Neonates After Neonatal Heart Surgery

PubMed Central

Jalali, Ali; Licht, Daniel J.; Nataraj, C.

2013-01-01

This paper is concerned with the prediction of the occurrence of Periventricular Leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after the cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. The decision tree classification technique has been selected as a tool for prediction of the PVL because of its capacity for discovering rules and novel associations in the data. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected from Children's Hospital of Philadelphia (CHOP) are used for this study. Vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), blood hemoglobin (Hb), hemoglobin oxygen content (HbO2), oxygen saturation (SpO2) and relative cerebral blood flow (rCBF). Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and min and max of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in blood gas data, maximum rate of change in the concentration of bicarbonate ions in blood (HCO3) and minimum rate of change in the partial pressure of dissolved CO2 in the blood (PaCO2) are the most important factors for prediction of the PVL. Among vital features the kurtosis of HR and Hb are the most important parameters. PMID:23367279

Schistosomiasis in Zambia: a systematic review of past and present experiences.

PubMed

Kalinda, Chester; Chimbari, Moses J; Mukaratirwa, Samson

2018-04-30

The speedy rate of change in the environmental and socio-economics factors may increase the incidence, prevalence and risk of schistosomiasis infections in Zambia. However, available information does not provide a comprehensive understanding of the biogeography and distribution of the disease, ecology and population dynamics of intermediate host snails. The current study used an information-theoretical approach to understand the biogeography and prevalence schistosomiasis and identified knowledge gaps that would be useful to improve policy towards surveillance and eradication of intermediate hosts snails in Zambia. To summarise the existing knowledge and build on past and present experiences of schistosomiasis epidemiology for effective disease control in Zambia, a systematic search of literature for the period 2000-2017 was done on PubMed, Google Scholar and EBSCOhost. Using the key words: 'Schistosomiasis', 'Biomphalaria', 'Bulinus', 'Schistosoma mansoni', 'Schistosoma haematobium', and 'Zambia', in combination with Booleans terms 'AND' and 'OR', published reports/papers were obtained and reviewed independently for inclusion. Thirteen papers published in English that fulfilled the inclusion criteria were selected for the final review. The papers suggest that the risk of infection has increased over the years and this has been attributed to environmental, socio-economic and demographic factors. Furthermore, schistosomiasis is endemic in many parts of the country with infection due to Schistosoma haematobium being more prevalent than that due to S. mansoni. This review also found that S. haematobium was linked to genital lesions, thus increasing risks of contracting other diseases such as HIV and cervical cancer. For both S. haematobium and S. mansoni, environmental, socio-economic, and demographic factors were influential in the transmission and prevalence of the disease and highlight the need for detailed knowledge on ecological modelling and mapping the

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.

Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption.

PubMed

Newby, Danielle; Freitas, Alex A; Ghafourian, Taravat

2015-01-27

Oral absorption of compounds depends on many physiological, physiochemical and formulation factors. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of human intestinal absorption. Despite this, the nature and exact characteristics of the relationship between these parameters are not well understood. In this study a large dataset of human intestinal absorption was collated along with in vitro permeability, aqueous solubility, melting point, and maximum dose for the same compounds. The dataset allowed a permeability threshold to be established objectively to predict high or low intestinal absorption. Using this permeability threshold, classification decision trees incorporating a solubility-related parameter such as experimental or predicted solubility, or the melting point based absorption potential (MPbAP), along with structural molecular descriptors were developed and validated to predict oral absorption class. The decision trees were able to determine the individual roles of permeability and solubility in oral absorption process. Poorly permeable compounds with high solubility show low intestinal absorption, whereas poorly water soluble compounds with high or low permeability may have high intestinal absorption provided that they have certain molecular characteristics such as a small polar surface or specific topology. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

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...

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.

The reach and impact of social marketing and reproductive health communication campaigns in Zambia

PubMed Central

Van Rossem, Ronan; Meekers, Dominique

2007-01-01

Background Like many sub-Saharan African countries, Zambia is dealing with major health issues, including HIV/AIDS, family planning, and reproductive health. To address reproductive health problems and the HIV/AIDS epidemic in Zambia, several social marketing and health communication programs focusing on reproductive and HIV/AIDS prevention programs are being implemented. This paper describes the reach of these programs and assesses their impact on condom use. Methods This paper assesses the reach of selected radio and television programs about family planning and HIV/AIDS and of communications about the socially marketed Maximum condoms in Zambia, as well as their impact on condom use, using data from the 2001–2002 Zambia Demographic and Health Survey. To control for self-selection and endogeneity, we use a two-stage regression model to estimate the effect of program exposure on the behavioural outcomes. Results Those who were exposed to radio and television programs about family planning and HIV/AIDS were more likely to have ever used a condom (OR = 1.16 for men and 1.06 for women). Men highly exposed to Maximum condoms social marketing communication were more likely than those with low exposure to the program to have ever used a condom (OR = 1.48), and to have used a condom at their last sexual intercourse (OR = 1.23). Conclusion Findings suggest that the reproductive health and social marketing campaigns in Zambia reached a large portion of the population and had a significant impact on condom use. The results suggest that future reproductive health communication campaigns that invest in radio programming may be more effective than those investing in television programming, and that future campaigns should seek to increase their impact among women, perhaps by focusing on the specific constrains that prevent females from using condoms. PMID:18088437

The reach and impact of social marketing and reproductive health communication campaigns in Zambia.

PubMed

Van Rossem, Ronan; Meekers, Dominique

2007-12-18

Like many sub-Saharan African countries, Zambia is dealing with major health issues, including HIV/AIDS, family planning, and reproductive health. To address reproductive health problems and the HIV/AIDS epidemic in Zambia, several social marketing and health communication programs focusing on reproductive and HIV/AIDS prevention programs are being implemented. This paper describes the reach of these programs and assesses their impact on condom use. This paper assesses the reach of selected radio and television programs about family planning and HIV/AIDS and of communications about the socially marketed Maximum condoms in Zambia, as well as their impact on condom use, using data from the 2001-2002 Zambia Demographic and Health Survey. To control for self-selection and endogeneity, we use a two-stage regression model to estimate the effect of program exposure on the behavioural outcomes. Those who were exposed to radio and television programs about family planning and HIV/AIDS were more likely to have ever used a condom (OR = 1.16 for men and 1.06 for women). Men highly exposed to Maximum condoms social marketing communication were more likely than those with low exposure to the program to have ever used a condom (OR = 1.48), and to have used a condom at their last sexual intercourse (OR = 1.23). Findings suggest that the reproductive health and social marketing campaigns in Zambia reached a large portion of the population and had a significant impact on condom use. The results suggest that future reproductive health communication campaigns that invest in radio programming may be more effective than those investing in television programming, and that future campaigns should seek to increase their impact among women, perhaps by focusing on the specific constrains that prevent females from using condoms.

A cost-effectiveness analysis of artemether lumefantrine for treatment of uncomplicated malaria in Zambia

PubMed Central

Chanda, Pascalina; Masiye, Felix; Chitah, Bona M; Sipilanyambe, Naawa; Hawela, Moonga; Banda, Patrick; Okorosobo, Tuoyo

2007-01-01

Background Malaria remains a leading cause of morbidity, mortality and non-fatal disability in Zambia, especially among children, pregnant women and the poor. Data gathered by the National Malaria Control Centre has shown that recently observed widespread treatment failure of SP and chloroquine precipitated a surge in malaria-related morbidity and mortality. As a result, the Government has recently replaced chloroquine and SP with combination therapy as first-line treatment for malaria. Despite the acclaimed therapeutic advantages of ACTs over monotherapies with SP and CQ, the cost of ACTs is much greater, raising concerns about affordability in many poor countries such as Zambia. This study evaluates the cost-effectiveness analysis of artemether-lumefantrine, a version of ACTs adopted in Zambia in mid 2004. Methods Using data gathered from patients presenting at public health facilities with suspected malaria, the costs and effects of using ACTs versus SP as first-line treatment for malaria were estimated. The study was conducted in six district sites. Treatment success and reduction in demand for second line treatment constituted the main effectiveness outcomes. The study gathered data on the efficacy of, and compliance to, AL and SP treatment from a random sample of patients. Costs are based on estimated drug, labour, operational and capital inputs. Drug costs were based on dosages and unit prices provided by the Ministry of Health and the manufacturer (Norvatis). Findings The results suggest that AL produces successful treatment at less cost than SP, implying that AL is more cost-effective. While it is acknowledged that implementing national ACT program will require considerable resources, the study demonstrates that the health gains (treatment success) from every dollar spent are significantly greater if AL is used rather than SP. The incremental cost-effectiveness ratio is estimated to be US$4.10. When the costs of second line treatment are considered the

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.

[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.

Developing a community driven sustainable model of maternity waiting homes for rural Zambia.

PubMed

Lori, Jody R; Munro-Kramer, Michelle L; Mdluli, Eden Ahmed; Musonda Mrs, Gertrude K; Boyd, Carol J

2016-10-01

maternity waiting homes (MWHs) are residential dwellings located near health facilities where women in the late stages of pregnancy stay to await childbirth and receive immediate postpartum services. These shelters help overcome distance and transportation barriers that prevent women from receiving timely skilled obstetric care. the purpose of this study was to explore Zambian stakeholders' beliefs regarding the acceptability, feasibility, and sustainability of maternity waiting homes (MWHs) to inform a model for rural Zambia. a qualitative design using a semi-structured interview guide for data collection was used. two rural districts in the Eastern province of Zambia. individual interviews were conducted with community leaders (n=46). Focus groups were held with Safe Motherhood Action Groups, husbands, and women of childbearing age in two rural districts in Zambia (n=500). latent content analysis was used to analyze the data. participants were overwhelmingly in support of MWHs as a way to improve access to facility-based childbirth and address the barrier of distance. Data suggest that participants can describe features of high quality care, and the type of care they expect from a MWH. Stakeholders acknowledged the need to contribute to the maintenance of the MWH, and that community involvement was crucial to MWH sustainability. access to facility childbirth remains particularly challenging in rural Zambia and delays in seeking care exist. Maternity waiting homes offer a feasible and acceptable intervention to reduce delays in seeking care, thereby holding the potential to improve maternal outcomes. this study joins a growing literature on the acceptability, feasibility, and sustainability of MWHs. It is believed that MWHs, by addressing the distance and transportation barriers, will increase the use of skilled birth attendants, thereby reducing maternal and neonatal morbidity and mortality in rural, low resource areas of Zambia. We recommend that any initiative

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.

Investment incentives and the implementation of the Framework Convention on Tobacco Control: evidence from Zambia.

PubMed

Lencucha, Raphael; Drope, Jeffrey; Labonte, Ronald; Zulu, Richard; Goma, Fastone

2016-07-01

Policy misalignment across different sectors of government serves as one of the pivotal barriers to WHO Framework Convention on Tobacco Control (FCTC) implementation. This paper examines the logic used by government officials to justify investment incentives to increase tobacco processing and manufacturing in the context of FCTC implementation in Zambia. We conducted qualitative semistructured interviews with key informants from government, civil society and intergovernmental economic organisations (n=23). We supplemented the interview data with an analysis of public documents pertaining to the policy of economic development in Zambia. We found gross misalignments between the policies of the economic sector and efforts to implement the provisions of the FCTC. Our interviews uncovered the rationale used by officials in the economic sector to justify providing economic incentives to bolster tobacco processing and manufacturing in Zambia: (1) tobacco is not consumed by Zambians/tobacco is an export commodity, (2) economic benefits outweigh health costs and (3) tobacco consumption is a personal choice. Much of the struggle Zambia has experienced in implementing the FCTC can be attributed to misalignments between the economic and health sectors. Zambia's development agenda seeks to bolster agricultural processing and manufacturing. Tobacco control proponents must recognise and work within this context in order to foster productive strategies with those working on tobacco supply issues. These findings are broadly applicable to the global context. It is important that the Ministry of Health monitors the tobacco policy of and engages with these sectors to find ways of harmonising FCTC implementation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Price, tax and tobacco product substitution in Zambia.

PubMed

Stoklosa, Michal; Goma, Fastone; Nargis, Nigar; Drope, Jeffrey; Chelwa, Grieve; Chisha, Zunda; Fong, Geoffrey T

2018-03-24

In Zambia, the number of cigarette users is growing, and the lack of strong tax policies is likely an important cause. When adjusted for inflation, levels of tobacco tax have not changed since 2007. Moreover, roll-your-own (RYO) tobacco, a less-costly alternative to factory-made (FM) cigarettes, is highly prevalent. We modelled the probability of FM and RYO cigarette smoking using individual-level data obtained from the 2012 and 2014 waves of the International Tobacco Control (ITC) Zambia Survey. We used two estimation methods: the standard estimation method involving separate random effects probit models and a method involving a system of equations (incorporating bivariate seemingly unrelated random effects probit) to estimate price elasticities of FM and RYO cigarettes and their cross-price elasticities. The estimated price elasticities of smoking prevalence are -0.20 and -0.03 for FM and RYO cigarettes, respectively. FM and RYO are substitutes; that is, when the price of one of the products goes up, some smokers switch to the other product. The effects are stronger for substitution from FM to RYO than vice versa. This study affirms that increasing cigarette tax with corresponding price increases could significantly reduce cigarette use in Zambia. Furthermore, reducing between-product price differences would reduce substitution from FM to RYO. Since RYO use is associated with lower socioeconomic status, efforts to decrease RYO use, including through tax/price approaches and cessation assistance, would decrease health inequalities in Zambian society and reduce the negative economic consequences of tobacco use experienced by the poor. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The prevalence and distribution of schistosomiasis in Zambia.

PubMed

Boatin, B A; Wurapa, F K; Ulrich, A M

1985-09-01

This paper attempts to approach an accurate report of prevalence of schistosomiasis in Zambia by bringing together several reports. A review of some early prevalence studies in Zambia shows the prevalence of S. hematobium infection to be (14-40%) and that for S. mansoni to range from (0-7%), in the Northern and Luapula Provinces. The areas around Lakes Kariba in the south, and Bangweulu in the north had prevalence rates of (3-35%) for S. hematobium and (2-6%) for S. mansoni. A nationwide survey found the overall prevalence of S. hematobium to be about 16%. The Gwembe Valley in the South had the highest prevalence of 57.9% for S. hematobium; S. mansoni with a prevalence of (45-77%) in the Northern Province from more recent studies is not very widespread. A comprehensive study performed between 1969-73 covered almost the entire rural population and found an overall prevalence of 16.8%, varying greatly between ecozones. The 5-14 year age group showed the highest prevalence. A 1976-82 study of rural primary school children in several provinces found high prevalence rates. Specimen gathering and analysis is described for most studies analyzed, revealing some inconsistencies threatening the reliability of data. Available data do show the spotty and local nature of the prevalence rates between areas. There have not been many studies of S. mansoni prevalence, possibly due to the difficulties involved with the collection of stool specimens, but prevalence (especially seasonal) has been shown to be high in certain areas (although low generally). The areas around the 2 major lakes show considerable prevalence of both parasites, and further study is needed on the health impact of man-made lakes in Zambia and elsewhere.

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.

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

Socio-cultural factors surrounding mental distress during the perinatal period in Zambia: a qualitative investigation

PubMed Central

2012-01-01

Background The presence of mental distress during pregnancy and after childbirth imposes detrimental developmental and health consequences for families in all nations. In Zambia, the Ministry of Health (MoH) has proposed a more comprehensive approach towards mental health care, recognizing the importance of the mental health of women during the perinatal period. Aim The study explores factors contributing to mental distress during the perinatal period of motherhood in Zambia. Methods A qualitative study was conducted in Lusaka, Zambia with nineteen focus groups comprising 149 women and men from primary health facilities and schools respectively. Findings There are high levels of mental distress in four domains: worry about HIV status and testing; uncertainty about survival from childbirth; lack of social support; and vulnerability/oppression. Conclusion Identifying mental distress and prompt referral for interventions is critical to improving the mental health of the mother and prevent the effects of mental distress on the baby. Recommendation Strategies should be put in place to ensure pregnant women are screened for possible perinatal mental health problems during their visit to antenatal clinic and referral made to qualified mental health professionals. In addition further research is recommended in order to facilitate evidence based mental health policy formulation and implementation in Zambia. PMID:22954173

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

Infant-mother and infant-sibling attachment in Zambia.

PubMed

Mooya, Haatembo; Sichimba, Francis; Bakermans-Kranenburg, Marian

2016-12-01

This study, the first in Zambia using the Strange Situation Procedure (SSP) to observe attachment relationships and the "very first" observational study of infant-sibling attachment, examined patterns of infant-mother and infant-sibling attachment, and tested their association. We included siblings who were substantially involved in caregiving activities with their younger siblings. We hypothesized that infants would develop attachment relationships to both mothers and siblings; the majority of infants would be classified as securely attached to both caregivers, and infant-mother and infant-sibling attachment would be unrelated. The sample included 88 low-income families in Lusaka, Zambia (average of 3.5 children; SD = 1.5). The SSP distributions (infant-mother) were 59% secure, 24% avoidant and 17% resistant, and 46% secure, 20% avoidant, 5% resistant and 29% disorganized for three- and four-way classifications, respectively. The infant-sibling classifications were 42% secure, 23% avoidant and 35% resistant, and 35% secure, 23% avoidant, 9% resistant and 33% disorganized for three- and four-way classifications, respectively. Infant-mother and infant-sibling attachment relationships were not associated.

Mycobacterium bovis infection at the interface between domestic and wild animals in Zambia.

PubMed

Hang'ombe, Mudenda B; Munyeme, Musso; Nakajima, Chie; Fukushima, Yukari; Suzuki, Haruka; Matandiko, Wigganson; Ishii, Akihiro; Mweene, Aaron S; Suzuki, Yasuhiko

2012-11-14

In Zambia, the presence of bovine tuberculosis in both wild and domestic animals has long been acknowledged and mutual transmission between them has been predicted without any direct evidence. Elucidation of the circulating Mycobacterium bovis strains at wild and domestic animals interphase area in Zambia, where bovine tuberculosis was diagnosed in wildlife seemed to be important. A PCR identified 15 and 37 M. bovis isolates from lechwe and cattle, respectively. Spoligotype analysis revealed that M. bovis strains from lechwe and cattle in Kafue basin clustered into a major node SB0120, where isolates outside the Kafue basin clustered into different nodes of SB0131 and SB0948. The comparatively higher variety of strains in cattle compared to lechwe elucidated by Mycobacterial Interspersed Repetitive Units-Variable Number Tandem Repeats analyses are consistent with cattle being the probable source of M. bovis in wild and domestic animals interphase area in Zambia. These results provide strong evidence of M. bovis strains transfer between cattle and lechwe, with the latter having developed into a sylvatic reservoir host.

Mycobacterium bovis infection at the interface between domestic and wild animals in Zambia

PubMed Central

2012-01-01

Background In Zambia, the presence of bovine tuberculosis in both wild and domestic animals has long been acknowledged and mutual transmission between them has been predicted without any direct evidence. Elucidation of the circulating Mycobacterium bovis strains at wild and domestic animals interphase area in Zambia, where bovine tuberculosis was diagnosed in wildlife seemed to be important. Results A PCR identified 15 and 37 M. bovis isolates from lechwe and cattle, respectively. Spoligotype analysis revealed that M. bovis strains from lechwe and cattle in Kafue basin clustered into a major node SB0120, where isolates outside the Kafue basin clustered into different nodes of SB0131 and SB0948. The comparatively higher variety of strains in cattle compared to lechwe elucidated by Mycobacterial Interspersed Repetitive Units–Variable Number Tandem Repeats analyses are consistent with cattle being the probable source of M. bovis in wild and domestic animals interphase area in Zambia. Conclusions These results provide strong evidence of M. bovis strains transfer between cattle and lechwe, with the latter having developed into a sylvatic reservoir host. PMID:23151267

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.

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…

The influence of gender and age on choice of flood adaptation strategies: A case study of Zambia and Namibia..

NASA Astrophysics Data System (ADS)

Mabuku, Monde

2017-04-01

It is reported that flood events will increase due to variability and change in climate, thus increasing the number of people exposed to flooding disasters. This exposure negatively impacts rural households' livelihoods. Women, men, young, old has distinctive vulnerability and this shapes the choice of flood adaptation strategies. This calls for a need to adopt group specific interventions to strengthen local adaptive capacity to flooding for the affected population. The purpose of this case study was to determine the adaptation strategies to floods adopted by rural households in the Zambezi region of Namibia and Mwandi district of Zambia. The study further examined how gender and age influenced the choice of different adaptation strategies. Six focus group meetings and a questionnaire survey of 207 randomly sampled households were conducted in the flood prone areas of the study. Descriptive statistics results on the adaptation strategies indicated that a majority of the households in Namibia learnt to live with floods (86%),practiced mafisa cattle trade (86%), flood water harvesting (68%), practiced early and late planting (63%), prayed (55%), practiced conservation agriculture (54%) and fish farming (53%). In Zambia the adaptation strategies were; conservation agriculture (91%), acquiring better skills on preparedness (66%), flood water harvesting (63%), praying (60%), and flood proofing (52%). Logistic regression analysis showed that age positively and significantly influenced the likelihood of taking up adaptation strategies such as tree planting, relocation to higher ground, flood water harvesting, early and late planting. The older the respondents the more likely they were to adopt the strategies mentioned. More young ones were more likely to adopt acquiring better skills on flood preparedness and mafisa cattle trading than the old ones. Gender positively and significantly influenced mafisa cattle trade (p<0.01), male headed households were more likely to

Catholic Education in Zambia: Mission Integrity and Politics

ERIC Educational Resources Information Center

Carmody, Brendan

2016-01-01

This article provides the history of Catholic state-aided schooling in Zambia for over a century. It notes how the Catholic Church came to view its school to be a pivotal means of church development. By cooperation with the state it entered more fully into the nation's future by offering high-quality state-sponsored schooling. This proved to…

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

PubMed

Jin, Mingwu; Deng, Weishu

2018-05-15

There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different disease stages using brain structural information provided by magnetic resonance imaging (MRI) data. The neighborhood component analysis (NCA) is applied to select most powerful features for prediction. The ensemble decision tree classifier is built to predict which group the subject belongs to. The best features and model parameters are determined by cross validation of the training data. Our results show that 16 out of a total of 429 features were selected by NCA using 240 training subjects, including MMSE score and structural measures in memory-related regions. The boosting tree model with NCA features can achieve prediction accuracy of 56.25% on 160 test subjects. Principal component analysis (PCA) and sequential feature selection (SFS) are used for feature selection, while support vector machine (SVM) is used for classification. The boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods. The results suggest that NCA be a better feature selection strategy than PCA and SFS for the data used in this study. Ensemble tree classifier with boosting is more powerful than SVM to predict the subject group. However, more advanced feature selection and classification methods or additional measures besides structural MRI may be needed to improve the prediction performance. Copyright © 2018 Elsevier B.V. All rights reserved.

Classification of Different Degrees of Disability Following Intracerebral Hemorrhage: A Decision Tree Analysis from VISTA-ICH Collaboration.

PubMed

Phan, Thanh G; Chen, Jian; Beare, Richard; Ma, Henry; Clissold, Benjamin; Van Ly, John; Srikanth, Velandai

2017-01-01

Prognostication following intracerebral hemorrhage (ICH) has focused on poor outcome at the expense of lumping together mild and moderate disability. We aimed to develop a novel approach at classifying a range of disability following ICH. The Virtual International Stroke Trial Archive collaboration database was searched for patients with ICH and known volume of ICH on baseline CT scans. Disability was partitioned into mild [modified Rankin Scale (mRS) at 90 days of 0-2], moderate (mRS = 3-4), and severe disabilities (mRS = 5-6). We used binary and trichotomy decision tree methodology. The data were randomly divided into training (2/3 of data) and validation (1/3 data) datasets. The area under the receiver operating characteristic curve (AUC) was used to calculate the accuracy of the decision tree model. We identified 957 patients, age 65.9 ± 12.3 years, 63.7% males, and ICH volume 22.6 ± 22.1 ml. The binary tree showed that lower ICH volume (<13.7 ml), age (<66.5 years), serum glucose (<8.95 mmol/l), and systolic blood pressure (<170 mm Hg) discriminate between mild versus moderate-to-severe disabilities with AUC of 0.79 (95% CI 0.73-0.85). Large ICH volume (>27.9 ml), older age (>69.5 years), and low Glasgow Coma Scale (<15) classify severe disability with AUC of 0.80 (95% CI 0.75-0.86). The trichotomy tree showed that ICH volume, age, and serum glucose can separate mild, moderate, and severe disability groups with AUC 0.79 (95% CI 0.71-0.87). Both the binary and trichotomy methods provide equivalent discrimination of disability outcome after ICH. The trichotomy method can classify three categories at once, whereas this action was not possible with the binary method. The trichotomy method may be of use to clinicians and trialists for classifying a range of disability in ICH.

New Agricultural Settlement, Meheba River, Zambia, Africa

NASA Technical Reports Server (NTRS)

1990-01-01

This infra-red view of a new settlement along the Meheba River, Zambia, Africa (12.5S, 26.0E) resembles the resettlement clusters in the Amazon basin of Brazil. However, this settlement is on savanna land not a tropical forest region, so relatively little land clearing was required. The familiar pattern of small single family plots, no large commercial fields, along the branches of a herringbone road network is evident.

Task sharing in Zambia: HIV service scale-up compounds the human resource crisis.

PubMed

Walsh, Aisling; Ndubani, Phillimon; Simbaya, Joseph; Dicker, Patrick; Brugha, Ruairí

2010-09-17

health worker numbers. The findings are based on an analysis of routine data that are available to district and national managers. Mixed methods research is needed, combining quantitative analyses of routine health information with follow-up qualitative interviews, to explore and explain workload changes, and to identify and measure where problems are most acute, so that decision makers can respond appropriately. This study provides quantitative evidence of a human resource crisis in health facilities in Zambia, which may be more acute in rural areas.

Traditional Chinese medicine pharmacovigilance in signal detection: decision tree-based data classification.

PubMed

Wei, Jian-Xiang; Wang, Jing; Zhu, Yun-Xia; Sun, Jun; Xu, Hou-Ming; Li, Ming

2018-03-09

Traditional Chinese Medicine (TCM) is a style of traditional medicine informed by modern medicine but built on a foundation of more than 2500 years of Chinese medical practice. According to statistics, TCM accounts for approximately 14% of total adverse drug reaction (ADR) spontaneous reporting data in China. Because of the complexity of the components in TCM formula, which makes it essentially different from Western medicine, it is critical to determine whether ADR reports of TCM should be analyzed independently. Reports in the Chinese spontaneous reporting database between 2010 and 2011 were selected. The dataset was processed and divided into the total sample (all data) and the subsample (including TCM data only). Four different ADR signal detection methods-PRR, ROR, MHRA and IC- currently widely used in China, were applied for signal detection on the two samples. By comparison of experimental results, three of them-PRR, MHRA and IC-were chosen to do the experiment. We designed several indicators for performance evaluation such as R (recall ratio), P (precision ratio), and D (discrepancy ratio) based on the reference database and then constructed a decision tree for data classification based on such indicators. For PRR: R 1 -R 2  = 0.72%, P 1 -P 2  = 0.16% and D = 0.92%; For MHRA: R 1 -R 2  = 0.97%, P 1 -P 2  = 0.20% and D = 1.18%; For IC: R 1 -R 2  = 1.44%, P 2 -P 1  = 4.06% and D = 4.72%. The threshold of R,Pand Dis set as 2%, 2% and 3% respectively. Based on the decision tree, the results are "separation" for PRR, MHRA and IC. In order to improve the efficiency and accuracy of signal detection, we suggest that TCM data should be separated from the total sample when conducting analyses.

Ethnoveterinary treatments for common cattle diseases in four districts of the Southern Province, Zambia.

PubMed

Syakalima, Michelo; Simuunza, Martin; Zulu, Victor Chisha

2018-02-01

Ethno veterinary knowledge has rarely been recorded, and no or limited effort has been made to exploit this knowledge despite its widespread use in Zambia. This study documented the types of plants used to treat important animal diseases in rural Zambia as a way of initiating their sustained documentation and scientific validation. The study was done in selected districts of the Southern Zambia, Africa. The research was a participatory epidemiological study conducted in two phases. The first phase was a pre-study exploratory rapid rural appraisal conducted to familiarize the researchers with the study areas, and the second phase was a participatory rural appraisal to help gather the data. The frequency index was used to rank the commonly mentioned treatments. A number of diseases and traditional treatments were listed with the help of local veterinarians. Diseases included: Corridor disease (Theileriosis), foot and mouth disease, blackleg, bloody diarrhea, lumpy skin disease, fainting, mange, blindness, coughing, bloat, worms, cobra snakebite, hemorrhagic septicemia, and transmissible venereal tumors. The plant preparations were in most diseases given to the livestock orally (as a drench). Leaves, barks, and roots were generally used depending on the plant type. Ethno veterinary medicine is still widespread among the rural farmers in the province and in Zambia in general. Some medicines are commonly used across diseases probably because they have a wide spectrum of action. These medicines should, therefore, be validated for use in conventional livestock healthcare systems in the country to reduce the cost of treatments.

A rapid assessment of avoidable blindness in Southern Zambia.

PubMed

Lindfield, Robert; Griffiths, Ulla; Bozzani, Fiammetta; Mumba, Musonda; Munsanje, Joseph

2012-01-01

A rapid assessment of avoidable blindness (RAAB) was conducted in Southern Zambia to establish the prevalence and causes of blindness in order to plan effective services and advocate for support for eye care to achieve the goals of VISION 2020: the right to sight. Cluster randomisation was used to select villages in the survey area. These were further subdivided into segments. One segment was selected randomly and a survey team moved from house to house examining everyone over the age of 50 years. Each individual received a visual acuity assessment and simple ocular examination. Data was recorded on a standard proforma and entered into an established software programme for analysis. 2.29% of people over the age of 50 were found to be blind (VA <3/60 in the better eye with available correction). The major cause of blindness was cataract (47.2%) with posterior segment disease being the next main cause (18.8%). 113 eyes had received cataract surgery with 30.1% having a poor outcome (VA <6/60) following surgery. Cataract surgical coverage showed that men (72%) received more surgery than women (65%). The results from the RAAB survey in Zambia were very similar to the results from a similar survey in Malawi, where the main cause of blindness was cataract but posterior segment disease was also a significant contributor. Blindness in this part of Zambia is mainly avoidable and there is a need for comprehensive eye care services that can address both cataract and posterior segment disease in the population if the aim of VISION 2020 is to be achieved. Services should focus on quality and gender equity of cataract surgery.

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.

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%.

Mapping potential carbon and timber losses from hurricanes using a decision tree and ecosystem services driver model.

PubMed

Delphin, S; Escobedo, F J; Abd-Elrahman, A; Cropper, W

2013-11-15

Information on the effect of direct drivers such as hurricanes on ecosystem services is relevant to landowners and policy makers due to predicted effects from climate change. We identified forest damage risk zones due to hurricanes and estimated the potential loss of 2 key ecosystem services: aboveground carbon storage and timber volume. Using land cover, plot-level forest inventory data, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and a decision tree-based framework; we determined potential damage to subtropical forests from hurricanes in the Lower Suwannee River (LS) and Pensacola Bay (PB) watersheds in Florida, US. We used biophysical factors identified in previous studies as being influential in forest damage in our decision tree and hurricane wind risk maps. Results show that 31% and 0.5% of the total aboveground carbon storage in the LS and PB, respectively was located in high forest damage risk (HR) zones. Overall 15% and 0.7% of the total timber net volume in the LS and PB, respectively, was in HR zones. This model can also be used for identifying timber salvage areas, developing ecosystem service provision and management scenarios, and assessing the effect of other drivers on ecosystem services and goods. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Rural Implementation of a 52 Node Mixed Wireless Mesh Network in Macha, Zambia

NASA Astrophysics Data System (ADS)

Backens, Jonathan; Mweemba, Gregory; van Stam, Gertjan

In spite of increasing international and academic attention, there remains many challenges facing real world implementations of developing technologies. There has been considerable hype behind Wireless Mesh Networking as the ubiquitous solution for rural ICT in the developing world. In this paper, we present the real world rural mesh network implementation in the village of Macha, Zambia and draw both performance conclusions as well as overall experiential conclusions. The purpose of this paper is to introduce and analyze our low cost solution and extrapolate future trends for rural ICT implementations in Zambia.

'Between a rock and a hard place': applied anthropology and AIDS research on a commercial farm in Zambia.

PubMed

Bond, V

1997-01-01

Fieldwork on a commercial farm in southern Zambia, which was aimed at designing an HIV prevention program for farm workers, gradually exposed the nature of sexual liaisons between young girls, coming to work on the farm from the surrounding villages, and older migrant men workers. Before completing fieldwork, the anthropologist voiced her concern about the implications of these liaisons for the spread of STDs and HIV with the local rural community, farm management and farm workers. The immediate outcome of her intercessions was the decision by management to sack under-age workers. Although some members of the local community, including local research assistants, and some managers and workers welcomed this decision, others were angered by it. Caught between interest groups and conflicting guidelines, the anthropologist, it is argued, was in a no-win situation, 'between a rock and a hard place'. The paper proposes that the application of anthropological ethics in AIDS research needs some re-evaluation.

Cost Sharing in Zambia's Public Universities: Prospects and Challenges

ERIC Educational Resources Information Center

Masaiti, Gift; Shen, Hong

2013-01-01

This research paper explores the concept of "cost sharing" which became more prominent in Zambia education with the advent of democratic form of governance in 1991. As a way of responding to the ever diminishing tax revenues, government through the education policy of 1996, allowed higher education institutions including public…

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.

Enhancing global health and education in Malawi, Zambia, and the United States through an interprofessional global health exchange program.

PubMed

Wilson, Lynda Law; Somerall, D'Ann; Theus, Lisa; Rankin, Sally; Ngoma, Catherine; Chimwaza, Angela

2014-05-01

This article describes participant outcomes of an interprofessional collaboration between health professionals and faculty in Malawi, Zambia, and the United States (US). One strategy critical for improving global health and addressing Millennium Development goals is promotion of interprofessional education and collaboration. Program participants included 25 health professionals from Malawi and Zambia, and 19 faculty/health professionals from Alabama and California. African Fellows participated in a 2 week workshop on Interprofessional Education in Alabama followed by 2 weeks working on individual goals with faculty collaborators/mentors. The US Fellows also spent 2 weeks visiting their counterparts in Malawi and Zambia to develop plans for sustainable partnerships. Program evaluations demonstrated participants' satisfaction with the program and indicated that the program promoted interprofessional and cross-cultural understanding; fostered development of long-term sustainable partnerships between health professionals and educators in Zambia and the US; and created increased awareness and use of resources for global health education. © 2014.

Detection of Plasmodium falciparum Infection in Anopheles squamosus (Diptera: Culicidae) in an Area Targeted for Malaria Elimination, Southern Zambia

PubMed Central

Stevenson, Jennifer C.; Simubali, Limonty; Mbambara, Saidon; Musonda, Michael; Mweetwa, Sydney; Mudenda, Twig; Pringle, Julia C.; Jones, Christine M.; Norris, Douglas E.

2016-01-01

Southern Zambia is the focus of strategies to create malaria-free zones. Interventions being rolled out include test and treat strategies and distribution of insecticide-treated bed nets that target vectors that host-seek indoors and late at night. In Macha, Choma District, collections of mosquitoes were made outdoors using barrier screens within homesteads or UV bulb light traps set next to goats, cattle, or chickens during the rainy season of 2015. Anopheline mosquitoes were identified to species using molecular methods and Plasmodium falciparum infectivity was determined by ELISA and real-time qPCR methods. More than 40% of specimens caught were identified as Anopheles squamosus Theobald, 1901 of which six were found harboring malaria parasites. A single sample, morphologically identified as Anopheles coustani Laveran, 1900, was also found to be infectious. All seven specimens were caught outdoors next to goat pens. Parasite-positive specimens as well as a subset of An. squamosus specimens from either the same study or archive collections from the same area underwent sequencing of the mitochondrial cytochrome oxidase subunit I gene. Maximum parsimony trees constructed from the aligned sequences indicated presence of at least two clades of An. squamosus with infectious specimens falling in each clade. The single infectious specimen identified morphologically as An. coustani could not be matched to reference sequences. This is the first report from Zambia of infections in An. squamosus, a species which is described in literature to display exophagic traits. The bionomic characteristics of this species needs to be studied further to fully evaluate the implications for indoor-targeted vector control. PMID:27297214

Lithostratigraphical correlation of the Neoproterozoic Roan Supergroup from Shaba (Zaire) and Zambia, in the central African copper-cobalt metallogenic province

NASA Astrophysics Data System (ADS)

Cailteux, J.; Binda, P. L.; Katekesha, W. M.; Kampunzu, A. B.; Intiomale, M. M.; Kapenda, D.; Kaunda, C.; Ngongo, K.; Tshiauka, T.; Wendorff, M.

1994-11-01

New data on the lower Katangan sequences in Shaba (Zaire) and Zambia, collected during the 1989 and 1990 UNESCO-sponsored Geotraverses, reveal an important development on friction breccias throughout the Zambian Copperbelt, which still remains poorly documented, and shows that the Zairean and Zambian facies of the Roan Supergroup can be correlated in detail. As in Zaire, the deformation of Katangan terranes during the Lufilian orogeny produced important friction breccias in Zambia. Such breccias occur mostly between the upper part of the Lower Roan Supergroup and the Mwashya Group (R-4): above the shale with grit (RL3) at Konkola and Mindola, or within the Upper Roan Dolomite at Chambishi South, Muliashi and Nchanga. At Mufulira, a typical fragment of Shaba Mines Group was observed within a major heterogeneous tectonic breccia. This situation is similar to that reported at Kipapila (Kimpe) and Lubembe in Zaire, both located on the same tectonic trend as Mufulira. However, a continuous stratigraphical succession can be observed in Zambia from the basal unconformity to the Mwashya Group. Strong lithological similarities were found, formation by formation, between the Roan sequences of Zambia and Zaire. In particular, the complete Mines Group of Zaire (R-2) and the units from the RL6 to the RL4 in Zambia were deposited under comparable conditions of sedimentation and show a similar and correlatable evolution of lithologies. Furthermore, the overlying Dipeta Group (R-3) of Zaire and the RL3, RU2/RU1 of Zambia, are equally comparable. Above the Upper Roan Dolomite, Lower Mwashya dolomitic rocks, identical with the ones of Shaba, have been noted to occur in Zambia in stratigraphical continuity with the typical black shales of the Upper Mwashya. The correlation between the coarse clastics of the Zambian footwall (RL7) and the red dolomitic argillites and sandstones of the Zairean R.A.T. (Roches Argillo Talqueuses: R-1) remains uncertain. However these two sequences show

Changes in sexual behaviour and practice and HIV prevalence indicators among young people aged 15–24 years in Zambia: An in-depth analysis of the 2001–2002 and 2007 Zambia Demographic and Health Surveys

PubMed Central

Kembo, Joshua

2014-01-01

HIV and AIDS still pose a major public health problem to most countries in sub-Saharan Africa, Zambia included. The objective of the paper is to determine changes in selected sexual behaviour and practice and HIV prevalence indicators between 2001–2002 and 2007. We used the Demographic and Health Survey Indicators Database for the computation of the selected indicators. We further used STATA 10.0 to compute significance tests to test for statistical difference in the indicators. The results indicate some changes in sexual behaviour, as indicated by an increase in abstinence, use of condoms and the decrease in multiple partnerships. The overall percentage of abstinence among never-married young men and women aged 15–24 years in Zambia increased significantly by 15.2% (p = .000) and 5.9% (p = .001) respectively, between 2001–2002 and 2007. A statistically significant increase of 6.6% (p = .029) was observed in the percentage of young women who reported having used a condom during the last time they had had premarital sex. A statistically significant decrease of 11.0% (p = .000) and 1.4% (p = .000) was observed among young men and women, respectively, who reported having multiple partners in the preceding 12 months. The factorial decomposition using multivariate analysis reveals that the indicators which contributed to the statistically significant 2.6% decline in HIV prevalence among young women aged 15–24 years in Zambia include proportion reporting condom use during premarital sex (+6.6%), abstinence (+5.9%), sex before age 15 (– 4.5%), premarital sex (– 2.6%), sex before age 18 (– 2.4%) and proportion reporting multiple partnerships (– 1.4%). Remarkable strides have been achieved towards promoting responsible sexual behaviour and practice among young people in Zambia. Further research focusing on factors that predispose young women in Zambia to higher risk of infection from HIV is required. The results from this paper should be useful in the design

Diagnosis and genotyping of African swine fever viruses from 2015 outbreaks in Zambia.

PubMed

Thoromo, Jonas; Simulundu, Edgar; Chambaro, Herman M; Mataa, Liywalii; Lubaba, Caesar H; Pandey, Girja S; Takada, Ayato; Misinzo, Gerald; Mweene, Aaron S

2016-04-29

In early 2015, a highly fatal haemorrhagic disease of domestic pigs resembling African swine fever (ASF) occurred in North Western, Copperbelt, and Lusaka provinces of Zambia. Molecular diagnosis by polymerase chain reaction targeting specific amplification of p72 (B646L) gene of ASF virus (ASFV) was conducted. Fourteen out of 16 domestic pigs from the affected provinces were found to be positive for ASFV. Phylogenetic analyses based on part of the p72 and the complete p54 (E183L) genes revealed that all the ASFVs detected belonged to genotypes I and Id, respectively. Additionally, epidemiological data suggest that the same ASFV spread from Lusaka to other provinces possibly through uncontrolled and/or illegal pig movements. Although the origin of the ASFV that caused outbreaks in domestic pigs in Zambia could not be ascertained, it appears likely that the virus may have emerged from within the country or region, probably from a sylvatic cycle. It is recommended that surveillance of ASF, strict biosecurity, and quarantine measures be imposed in order to prevent further spread and emergence of new ASF outbreaks in Zambia.

Provision of Learning and Teaching Materials for Pupils with Visual Impairment: Results from a National Survey in Zambia

ERIC Educational Resources Information Center

Akakandelwa, Akakandelwa; Munsanje, Joseph

2012-01-01

The aim of this study was to determine the provision of learning and teaching materials for pupils with visual impairment in basic and high schools of Zambia. A survey approach utilizing a questionnaire, interviews and a review of the literature was adopted for the study. The findings demonstrated that most schools in Zambia did not provide…

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...

Ethnoveterinary treatments for common cattle diseases in four districts of the Southern Province, Zambia

PubMed Central

Syakalima, Michelo; Simuunza, Martin; Zulu, Victor Chisha

2018-01-01

Aim: Ethno veterinary knowledge has rarely been recorded, and no or limited effort has been made to exploit this knowledge despite its widespread use in Zambia. This study documented the types of plants used to treat important animal diseases in rural Zambia as a way of initiating their sustained documentation and scientific validation. Materials and Methods: The study was done in selected districts of the Southern Zambia, Africa. The research was a participatory epidemiological study conducted in two phases. The first phase was a pre-study exploratory rapid rural appraisal conducted to familiarize the researchers with the study areas, and the second phase was a participatory rural appraisal to help gather the data. The frequency index was used to rank the commonly mentioned treatments. Results: A number of diseases and traditional treatments were listed with the help of local veterinarians. Diseases included: Corridor disease (Theileriosis), foot and mouth disease, blackleg, bloody diarrhea, lumpy skin disease, fainting, mange, blindness, coughing, bloat, worms, cobra snakebite, hemorrhagic septicemia, and transmissible venereal tumors. The plant preparations were in most diseases given to the livestock orally (as a drench). Leaves, barks, and roots were generally used depending on the plant type. Conclusion: Ethno veterinary medicine is still widespread among the rural farmers in the province and in Zambia in general. Some medicines are commonly used across diseases probably because they have a wide spectrum of action. These medicines should, therefore, be validated for use in conventional livestock healthcare systems in the country to reduce the cost of treatments. PMID:29657394

Quality of antenatal care in Zambia: a national assessment

PubMed Central

2012-01-01

Background Antenatal care (ANC) is one of the recommended interventions to reduce maternal and neonatal mortality. Yet in most Sub-Saharan African countries, high rates of ANC coverage coexist with high maternal and neonatal mortality. This disconnect has fueled calls to focus on the quality of ANC services. However, little conceptual or empirical work exists on the measurement of ANC quality at health facilities in low-income countries. We developed a classification tool and assessed the level of ANC service provision at health facilities in Zambia on a national scale and compared this to the quality of ANC received by expectant mothers. Methods We analysed two national datasets with detailed antenatal provider and user information, the 2005 Zambia Health Facility Census and the 2007 Zambia Demographic and Health Survey (DHS), to describe the level of ANC service provision at 1,299 antenatal facilities in 2005 and the quality of ANC received by 4,148 mothers between 2002 and 2007. Results We found that only 45 antenatal facilities (3%) fulfilled our developed criteria for optimum ANC service, while 47% of facilities provided adequate service, and the remaining 50% offered inadequate service. Although 94% of mothers reported at least one ANC visit with a skilled health worker and 60% attended at least four visits, only 29% of mothers received good quality ANC, and only 8% of mothers received good quality ANC and attended in the first trimester. Conclusions DHS data can be used to monitor “effective ANC coverage” which can be far below ANC coverage as estimated by current indicators. This “quality gap” indicates missed opportunities at ANC for delivering effective interventions. Evaluating the level of ANC provision at health facilities is an efficient way to detect where deficiencies are located in the system and could serve as a monitoring tool to evaluate country progress. PMID:23237601

Deschooling Language Study in East Africa: The Zambia Plan.

ERIC Educational Resources Information Center

Roberts, David Harrill

The second language learning methods of Southern Baptist missionaries in Zambia are described. Instead of studying the new language in a school setting, the student receives a week of orientation and is then placed in the community and expected to practice communicating with the native speakers at every opportunity. The student follows a course…

Moving Towards Inclusive Education Policies and Practices? Basic Education for AIDS Orphans and Other Vulnerable Children in Zambia

ERIC Educational Resources Information Center

Robson, Sue; Kanyanta, Sylvester Bonaventure

2007-01-01

The global spread of HIV and AIDS has presented a major threat to development, affecting the health of the poor and many aspects of social and economic development. The greatest impact of the epidemic has been felt in sub-Saharan Africa, and Zambia ranks among the worst hit countries. The Free Basic Education Policy in Zambia upholds the right of…

Disease constraints for utilization of the African buffalo (Syncerus caffer) on game ranches in Zambia.

PubMed

Munang'andu, Hetron M; Munag'andu, Hetron M; Siamudaala, Victor M; Nambota, Andrew; Bwalya, John M; Munyeme, Musso; Mweene, Aaron S; Takada, Ayato; Kida, Hiroshi

2006-05-01

Eco-tourism depending on wildlife is becoming increasingly profitable and landowners are beginning to favor game farming and ecotourism. In these areas, large-scale translocation of wildlife involves a diversity of species and large populations. The African buffalo (Syncerus caffer) is one of the major tourist attractions in Zambia. It accounts for 8.7% and 12.4% of the total animal species hunted in the Game Management Areas and the total hunting revenue earned in Zambia, respectively. It is ecologically an important animal species essential for the purpose of habitat control and facilitating the provision of suitable grazing pastures. However, the rearing of the African buffalo on game ranches has been hampered by its carrier state of the Southern Africa Terroritory (SAT) serotypes of foot and mouth disease virus (FMD). The African buffalo is also known to be a carrier of Theileria parva lawrencei, the causative agent of corridor disease (CD) that continues to have devastating effects on the livestock industry in Zambia. In addition, the importation of buffaloes from countries with populations endemic to bovine tuberculosis is highly restricted. Veterinary regulations in Zambia, strongly advocate against the translocation of buffaloes from protected areas to private ranches for disease control purposes thereby mounting a considerable constraint on the economic and ecological viability of the industry. It is hoped that this review will motivate the relevant government authorities in exploiting ways in which this animal species play a central role in eco-tourism.

A Qualitative Study of Migrant-related Stressors, Psychosocial Outcomes and HIV Risk Behavior among Truck Drivers in Zambia

PubMed Central

Ncube, Nomagugu; Simona, Simona J.; Kansankala, Brian; Sinkala, Emmanuel; Raidoo, Jasmin

2017-01-01

Truck drivers are part of mobile populations which have been noted as a key population at risk of HIV in Zambia. This study was aimed at 1) determining Potentially Traumatic Events (PTEs), labor migrant-related stressors, psychosocial problems and HIV risk behaviors among truck drivers in Zambia and 2) examining the relationship between PTEs, migrant-related stressors, psychosocial outcomes and HIV sexual risk behavior among truck drivers in Zambia. We conducted fifteen semi-structured interviews with purposively sampled male truck drivers at trucking companies in Lusaka, Zambia. Findings indicate that truck drivers experience multiple stressors and potentially traumatic incidences, including delays and long waiting hours at borders, exposure to crime and violence, poverty, stress related to resisting temptation of sexual interactions with sex workers or migrant women, and job-related safety concerns. Multiple psychosocial problems such as intimate partner violence, loneliness, anxiety and depression-like symptoms were noted. Transactional sex, coupled with inconsistent condom use were identified as HIV sexual risk behaviors. Findings suggest the critical need to develop HIV prevention interventions which account for mobility, potentially traumatic events, psychosocial problems, and the extreme fear of HIV testing among this key population. PMID:27681145

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.

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...

An Examination of Professionalism in the Zambia Army

DTIC Science & Technology

2014-12-12

corporateness . According to Huntington’s definition, professional officers should never intervene in politics, because officers would lose their...colonial Masters. Therefore, they depicted the African worker as powerless and devoid of self -awareness. This study seeks to put an officer in an African...the Zambia National Broadcasting Corporation radio station that he had taken over the reign of the country in a military coup. The coup was thwarted

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®

Predictors of suicidal ideation in older people: a decision tree analysis.

PubMed

Handley, Tonelle E; Hiles, Sarah A; Inder, Kerry J; Kay-Lambkin, Frances J; Kelly, Brian J; Lewin, Terry J; McEvoy, Mark; Peel, Roseanne; Attia, John R

2014-11-01

Suicide among older adults is a major public health issue worldwide. Although studies have identified psychological, physical, and social contributors to suicidal thoughts in older adults, few have explored the specific interactions between these factors. This article used a novel statistical approach to explore predictors of suicidal ideation in a community-based sample of older adults. Prospective cohort study. Participants aged 55-85 years were randomly selected from the Hunter Region, a large regional center in New South Wales, Australia. Baseline psychological, physical, and social factors, including psychological distress, physical functioning, and social support, were used to predict suicidal ideation at the 5-year follow-up. Classification and regression tree modeling was used to determine specific risk profiles for participants depending on their individual well-being in each of these key areas. Psychological distress was the strongest predictor, with 25% of people with high distress reporting suicidal ideation. Within high psychological distress, lower physical functioning significantly increased the likelihood of suicidal ideation, with high distress and low functioning being associated with ideation in 50% of cases. A substantial subgroup reported suicidal ideation in the absence of psychological distress; dissatisfaction with social support was the most important predictor among this group. The performance of the model was high (area under the curve: 0.81). Decision tree modeling enabled individualized "risk" profiles for suicidal ideation to be determined. Although psychological factors are important for predicting suicidal ideation, both physical and social factors significantly improved the predictive ability of the model. Assessing these factors may enhance identification of older people at risk of suicidal ideation. Copyright © 2014. Published by Elsevier Inc.

Preliminary Investigation of Trypanosomosis in Exotic Dog Breeds from Zambia's Luangwa and Zambezi Valleys Using LAMP

PubMed Central

Namangala, Boniface; Oparaocha, Elizabeth; Kajino, Kiichi; Hayashida, Kyoko; Moonga, Ladslav; Inoue, Noboru; Suzuki, Yasuhiko; Sugimoto, Chihiro

2013-01-01

Canine African trypanosomosis (CAT) is rarely reported in the literature. In this preliminary study, we evaluated the performance of loop-mediated isothermal amplification (LAMP) against microscopy to detect CAT in six exotic dog breeds naturally infected with trypanosomes from Zambia's South Luangwa National Park and Chiawa Game Management Area. To our knowledge, this is the first report of CAT in Zambia. The patients exhibited a variety of aspecific clinical signs. The LAMP did not only confirm all six parasitologically positive CAT cases detected passively between April 2010 and January 2012, but was also critical in trypanosome speciation. According to LAMP, the majority of the dogs had monolytic infections with either Trypanosoma congolense or Trypanosoma brucei rhodesiense. The LAMP is thus a potential simple and cost-effective tool for trypanosome diagnosis in endemic regions. The rare report of zoonotic trypanosomes in dogs in Zambia has public health implications and justifies further investigations of CAT. PMID:23716412

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.

An Integrated Hydro-Economic Model for Economy-Wide Climate Change Impact Assessment for Zambia

NASA Astrophysics Data System (ADS)

Zhu, T.; Thurlow, J.; Diao, X.

2008-12-01

Zambia is a landlocked country in Southern Africa, with a total population of about 11 million and a total area of about 752 thousand square kilometers. Agriculture in the country depends heavily on rainfall as the majority of cultivated land is rain-fed. Significant rainfall variability has been a huge challenge for the country to keep a sustainable agricultural growth, which is an important condition for the country to meet the United Nations Millennium Development Goals. The situation is expected to become even more complex as climate change would impose additional impacts on rainwater availability and crop water requirements, among other changes. To understand the impacts of climate variability and change on agricultural production and national economy, a soil hydrology model and a crop water production model are developed to simulate actual crop water uses and yield losses under water stress which provide annual shocks for a recursive dynamic computational general equilibrium (CGE) model developed for Zambia. Observed meteorological data of the past three decades are used in the integrated hydro-economic model for climate variability impact analysis, and as baseline climatology for climate change impact assessment together with several GCM-based climate change scenarios that cover a broad range of climate projections. We found that climate variability can explain a significant portion of the annual variations of agricultural production and GDP of Zambia in the past. Hidden beneath climate variability, climate change is found to have modest impacts on agriculture and national economy of Zambia around 2025 but the impacts would be pronounced in the far future if appropriate adaptations are not implemented. Policy recommendations are provided based on scenario analysis.

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

NASA Astrophysics Data System (ADS)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

Balancing risk, interpersonal intimacy and agency: perspectives from marginalised women in Zambia.

PubMed

Davis, Lwendo Moonzwe; Kostick, Kristin Marie

2018-05-15

Women are most exposed to sexual health risks within their marital relationships, primarily due to the sexually risky behaviours of their spouses. Studies show that expanding agency is critical for women to mitigate both physical and sexual health risks and is linked to increased psycho-social well-being and economic independence. Drawing on qualitative and quantitative primary data collected from a peri-urban community in Zambia, this paper explores how women exert agency in a community where few educational and economic opportunities and substantial food insecurity exacerbate women's risk for HIV within their marital relationships. It also examines how expressions of agency within marital unions can reduce HIV risk exposure and lead to socio-economic benefits. However, expressions of agency can also create physical, psycho-social and sexual health risks, particularly when spouses do not support independent decision-making and actions that women consider necessary to support the household and maintain intimacy. Findings highlight the importance of community involvement and addressing harmful socio-cultural norms to foster the realisation of women's agency.

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

Costs of facility-based HIV testing in Malawi, Zambia and Zimbabwe

PubMed Central

Mwenge, Lawrence; Sande, Linda; Mangenah, Collin; Ahmed, Nurilign; Kanema, Sarah; d’Elbée, Marc; Sibanda, Euphemia; Kalua, Thokozani; Ncube, Gertrude; Johnson, Cheryl C.; Hatzold, Karin; Cowan, Frances M.; Corbett, Elizabeth L.; Ayles, Helen; Maheswaran, Hendramoorthy

2017-01-01

Background Providing HIV testing at health facilities remains the most common approach to ensuring access to HIV treatment and prevention services for the millions of undiagnosed HIV-infected individuals in sub-Saharan Africa. We sought to explore the costs of providing these services across three southern African countries with high HIV burden. Methods Primary costing studies were undertaken in 54 health facilities providing HIV testing services (HTS) in Malawi, Zambia and Zimbabwe. Routinely collected monitoring and evaluation data for the health facilities were extracted to estimate the costs per individual tested and costs per HIV-positive individual identified. Costs are presented in 2016 US dollars. Sensitivity analysis explored key drivers of costs. Results Health facilities were testing on average 2290 individuals annually, albeit with wide variations. The mean cost per individual tested was US$5.03.9 in Malawi, US$4.24 in Zambia and US$8.79 in Zimbabwe. The mean cost per HIV-positive individual identified was US$79.58, US$73.63 and US$178.92 in Malawi, Zambia and Zimbabwe respectively. Both cost estimates were sensitive to scale of testing, facility staffing levels and the costs of HIV test kits. Conclusions Health facility based HIV testing remains an essential service to meet HIV universal access goals. The low costs and potential for economies of scale suggests an opportunity for further scale-up. However low uptake in many settings suggests that demand creation or alternative testing models may be needed to achieve economies of scale and reach populations less willing to attend facility based services. PMID:29036171

Provision and Management of Special Education in Community Schools: A Case of Donata, Malaikha and Shalom Community Schools in Zambia

ERIC Educational Resources Information Center

Mwamba, Mwenya N.

2016-01-01

Community schools appeared in Zambia in 1992 beginning with Lusaka and they quickly spread to other parts of the country. The Ministry of General Education recognizes its obligation to provide education of good quality to all children in response to national and international protocols to which Zambia is a part. The creation of Community Schools…

Structural Equation Model Trees

PubMed Central

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

2015-01-01

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 structures that separate a data set recursively into subsets with significantly different parameter estimates in a SEM. SEM Trees provide means for finding covariates and covariate interactions that predict differences in structural parameters in observed as well as in latent space and facilitate theory-guided exploration of empirical data. We describe the methodology, discuss theoretical and practical implications, and demonstrate applications to a factor model and a linear growth curve model. PMID:22984789

The utility of a classificatory decision tree approach to assist clinical differentiation of melancholic and non-melancholic depression.

PubMed

Parker, G; McCraw, S; Hadzi-Pavlovic, D

2015-07-15

Studies suggest that differentiating melancholic from non-melancholic depressive disorders is advanced by use of illness course as well as symptom variables but, in practice, potentially differentiating variables are generally positioned as having equal value. Judging that differentiating features are more likely to vary in their signal intensity, we sought to determine the number of features required to effect differentiation and their hierarchical order. The 24-item clinician-rated Sydney Melancholia Prototype Index (SMPI-CR) was completed for 364 unipolar depressed patients. The sample was divided into two cohorts according to the recruitment period. An RPART classification tree analysis identified the most discriminating SMPI items in the development sample of 197 patients, and examined the sensitivity and specificity of the diagnostic decisions, then sought to replicate findings in a validation sample of 169 patients. Independent analyses of putative SMPI items identified only seven items as required to discriminate those with clinically-diagnosed melancholic or non-melancholic depression when the conditions were examined separately. An RPART analysis considering differentiation of melancholic and non-melancholic depression in the total samples retained five of those items in the classification tree, three of which were non-symptom items, and with 92% sensitivity and 80% specificity in the development sample. This reduced item set showed 93% sensitivity and 82% specificity in the validation sample. Our clinical judgment of melancholic or non-melancholic depression may not correspond with the clinical logic employed by other clinicians. Only five SMPI items were required to derive a succinct and efficient decision tree, comprising high sensitivity and specificity in differentiating melancholic and non-melancholic depression. Current study findings provide an empirical model that could enrich clinicians׳ approach to differentiating melancholic and non

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.

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.

Utilization of focused antenatal care in Zambia: examining individual- and community-level factors using a multilevel analysis.

PubMed

Chama-Chiliba, Chitalu M; Koch, Steven F

2015-02-01

We examine the individual- and community-level factors associated with the utilization of antenatal care, following the adoption of the focused antenatal care (FANC) approach in Zambia. Using the 2007 Zambia Demographic and Health Survey, linked with administrative and health facility census data, we specify two multilevel logistic models to assess the factors associated with (1) the inadequate use of antenatal care (ANC) (defined as three or fewer visits) and (2) the non-use of ANC in the first trimester of pregnancy. Although all women in the selected sample had at least one ANC visit, 40% did not have the minimum number required (four), whereas more than 80% of the initial check-ups did not occur in the first trimester. At the individual level, the woman's employment status, quality of ANC received and the husband's educational attainment are negatively associated, while parity, the household childcare burden and wealth are positively associated with inadequate utilization of ANC. Both individual- and community-level characteristics influence inadequate use and non-use of ANC in the first trimester; however, community-level factors are relatively stronger in rural areas. The results suggest that improving the content of care during ANC visits may foster adequate use of ANC and encourage early initiation of ANC visits. Furthermore, health promotion programmes need to further encourage male involvement in pregnant women's decision to seek ANC to encourage adequate use of services. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2013; all rights reserved.

Liver fibrosis in treatment-naïve HIV-infected and HIV/HBV co-infected patients: Zambia and Switzerland compared.

PubMed

Wandeler, Gilles; Mulenga, Lloyd; Vinikoor, Michael J; Kovari, Helen; Battegay, Manuel; Calmy, Alexandra; Cavassini, Matthias; Bernasconi, Enos; Schmid, Patrick; Bolton-Moore, Carolyn; Sinkala, Edford; Chi, Benjamin H; Egger, Matthias; Rauch, Andri

2016-10-01

To examine the association between hepatitis B virus (HBV) infection and liver fibrosis in HIV-infected patients in Zambia and Switzerland. HIV-infected adults starting antiretroviral therapy in two clinics in Zambia and Switzerland were included. Liver fibrosis was evaluated using the aspartate aminotransferase-to-platelet-ratio index (APRI), with a ratio >1.5 defining significant fibrosis and a ratio >2.0 indicating cirrhosis. The association between hepatitis B surface antigen (HBsAg) positivity, HBV replication, and liver fibrosis was examined using logistic regression. In Zambia, 96 (13.0%) of 739 patients were HBsAg-positive compared to 93 (4.5%) of 2058 in Switzerland. HBsAg-positive patients were more likely to have significant liver fibrosis than HBsAg-negative ones: the adjusted odds ratio (aOR) was 3.25 (95% confidence interval (CI) 1.44-7.33) in Zambia and 2.50 (95% CI 1.19-5.25) in Switzerland. Patients with a high HBV viral load (≥20000 IU/ml) were more likely to have significant liver fibrosis compared to HBsAg-negative patients or patients with an undetectable viral load: aOR 3.85 (95% CI 1.29-11.44) in Zambia and 4.20 (95% CI 1.64-10.76) in Switzerland. In both settings, male sex was a strong risk factor for significant liver fibrosis. Despite the differences in HBV natural history between Sub-Saharan Africa and Europe, the degree of liver fibrosis and the association with important risk factors were similar. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

7 CFR 319.56-43 - Baby corn and baby carrots from Zambia.

Code of Federal Regulations, 2010 CFR

2010-01-01

... § 319.56-43 Baby corn and baby carrots from Zambia. (a) Immature, dehusked “baby” sweet corn (Zea mays L... consignments only. (b) Immature “baby” carrots (Daucus carota L. ssp. sativus) for consumption measuring 10 to...

Agriculture expansion, wood energy and woody encroachment in the Miombo woodlands: striving towards sustainability in Zambia.

NASA Astrophysics Data System (ADS)

Pelletier, J.

2017-12-01

Agricultural expansion is mostly done at the expense of forests and woodlands in the tropics. In Sub-Saharan Africa, forests are also critical as providers of wood energy for domestic consumption with a clear majority of households depending on firewood and charcoal as primary source of energy. Using Zambia as a case study, we look at the link between agricultural expansion, wood energy and the sustainability of forest resources. Zambia has been identified as having one of the highest rates of deforestation in the world, but there is large uncertainty in these estimates. The government of Zambia has identified charcoal production as one of the main of drivers of forest cover loss and is targeting this practice in their national strategy for reducing emissions from deforestation and forest degradation (REDD+). Other assessment however indicate that agricultural expansion is by far the main driver of deforestation and charcoal production is sustainable in Zambia. These competing evaluations call for a better understanding of the drivers of change. Using two national-scale vegetation surveys and remote sensing data, we compare and validate historical forest cover loss estimates to improve their accuracy. We attribute the change and their associated emissions to specific drivers of deforestation. The ecological properties of areas under change are compared to stable areas over time. Our results from national permanent plots indicate a woody encroachment process in Zambia, a potential ecological response to rising CO2 levels. We found that despite large emissions from deforestation, forests and woodlands have been acting as a carbon sink. This research addresses directly the potential feedbacks and responses to competing demands on forests coming from different sectors, including for agriculture and energy, to set the baseline on which to evaluate forest sustainability now and in the future given potentially new ecological conditions. It provides policy relevant

Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults

PubMed Central

Liu, Pei-Yang

2014-01-01

Metabolic syndrome (MetS) in young adults (age 20–39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population (n = 745). Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS. PMID:24817904

Gender equality and education: Increasing the uptake of HIV testing among married women in Kenya, Zambia and Zimbabwe.

PubMed

Singh, Kavita; Luseno, Winnie; Haney, Erica

2013-01-01

Gender equality and education are being promoted as strategies to combat the HIV epidemic in Africa, but few studies have looked at the role of gender equality and education in the uptake of a vital service - HIV testing. This study looks at the associations between education (a key input needed for gender equality) and key gender equality measures (financial decision making and attitudes toward violence) with ever tested for HIV and tested for HIV in the past year. The study focused on currently married women ages between15-24 and 25-34 in three countries - Kenya, Zambia, and Zimbabwe. The data came from the Demographic and Health Surveys. Logistic regression was used to study the role of gender equality and education on the HIV testing outcomes after controlling for both social and biological factors. Results indicated that education had a consistent positive relationship with testing for both age groups, and the associations were always significant for young women aged 15-24 years (p<0.01). The belief that gender-based violence is unacceptable was positively associated with testing for women aged 25-34 in all the three countries, although the associations were only significant in Kenya (among women reporting ever being tested: OR 1.58, p<0.00; among women reporting being tested in the past year: OR 1.34, p<0.05) and Zambia (among women reporting ever being tested: OR 1.24, p<0.10; among women reporting being tested in the past year: OR 1.29, p<0.05). High financial decision making was associated with testing for women aged 25-34 in Zimbabwe only (among women reporting ever being tested: OR 1.66, p<0.01). Overall, the findings indicate that the education and the promotion of gender equality are important strategies for increasing uptake of a vital HIV service, and thus are important tools for protecting girls and young women against HIV.

Application of decision tree for prediction of cutaneous leishmaniasis incidence based on environmental and topographic factors in Isfahan Province, Iran.

PubMed

Ramezankhani, Roghieh; Sajjadi, Nooshin; Nezakati Esmaeilzadeh, Roya; Jozi, Seyed Ali; Shirzadi, Mohammad Reza

2018-05-08

Cutaneous Leishmaniasis (CL) is a neglected tropical disease that continues to be a health problem in Iran. Nearly 350 million people are thought to be at risk. We investigated the impact of the environmental factors on CL incidence during the period 2007- 2015 in a known endemic area for this disease in Isfahan Province, Iran. After collecting data with regard to the climatic, topographic, vegetation coverage and CL cases in the study area, a decision tree model was built using the classification and regression tree algorithm. CL data for the years 2007 until 2012 were used for model construction and the data for the years 2013 until 2015 were used for testing the model. The Root Mean Square error and the correlation factor were used to evaluate the predictive performance of the decision tree model. We found that wind speeds less than 14 m/s, altitudes between 1234 and 1810 m above the mean sea level, vegetation coverage according to the normalized difference vegetation index (NDVI) less than 0.12, rainfall less than 1.6 mm and air temperatures higher than 30°C would correspond to a seasonal incidence of 163.28 per 100,000 persons, while if wind speed is less than 14 m/s, altitude less than 1,810 m and NDVI higher than 0.12, then the mean seasonal incidence of the disease would be 2.27 per 100,000 persons. Environmental factors were found to be important predictive variables for CL incidence and should be considered in surveillance and prevention programmes for CL control.

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/.

The influence of social constructs of hegemonic masculinity and sexual behaviour on acceptability of vaginal microbicides in Zambia.

PubMed

Mweemba, Oliver; Dixey, Rachael; Bond, Virginia; White, Alan

2018-07-01

Vaginal microbicides are heralded as a woman's HIV prevention method. This study, conducted in a microbicide clinical trial setting in Zambia, explored how the social construction of masculinity and sexual behaviour influenced the acceptability of vaginal microbicides. The data were generated from 18 In-depth Interviews and 8 Focus Group Discussions. The data were analysed thematically. The study found that hegemonic masculinity influenced the use of vaginal microbicides positively and negatively, in multiple ways including: decision to initiate gel use, autonomous use of the gel, and consistent use of the gel. Men were seen as heads of households and decision-makers who approved their partners' intentions to initiate gel use. Autonomous gel use by women was not supported because it challenged men's dominant position in sexual matters and at a family level. The socially accepted notion that men engaged in multiple sexual relationships also influenced women's decision to use the gel. Sustained gel use depended on the perceived effect of the gel on men's sexual desires, sexual performance, fertility, and sexual behaviour. This study suggests that acceptability of microbicides partially lies within the realm of men, with use constrained and dictated by cultural constructs and practice of masculinity and gender.

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,...

Is the Bangweulu Basin in Zambia the Eroded Remnant of a Large, Multiring Impact Crater?

NASA Astrophysics Data System (ADS)

Master, S.

1993-07-01

The Bangweulu Basin (BB) (ca. 29 degrees-31 degrees E, 10 degrees-12 degrees S) is a roughly circular depression, ~150 km in diameter, on the Bangweulu Block of Zambia. The basin, about 1148 m ASL, is occupied by Lakes Bangweulu (~85 km long) and Kampolombo (~20 km long) and the Bangweulu Swamps [1,2]. The basement consists partly of granitoids (~1.8 Ga) together with ~1.1-Ga Katangan cover rocks. To the north, cover rocks of the Mporokoso Group (~1.8-1.3 Ga) form the arcuate Luongo Fold Belt [3], partly defining the perimeter of the outermost ring (R = 125 km) of the Bangweulu structure. Drainage into the BB is centripetal, with one outlet in the south, draining into a tributary of the Luapula River, which then curves in a broad arc toward the north, along the Zambia-Zaire border, before entering Lake Mweru. Rivers entering the BB include the Luansenshi, which rises in the north and flows in an arc to the southeast and south before joining the Chambeshi River, which flows southwest, west, and northwest before entering Lake Bangweulu. There is an arcuate watershed in the west (at R = 100 km), to the west of which rivers drain to the southwest and west into the Luapula River. Several elongate curved sliver-like islands, including Mbawala (~30 x 4 km) and Chisi, are present in Lake Bangweulu. The curvature of the islands follows the arcuate northwest boundary of the lake in a concentric manner. Unlike all the other major lakes in Zambia and surrounding areas (Mweru, Tanganyika, Rukwa, Malawi, and Kariba), which occupy seismically active rift structures [4,5], the Bangweulu Basin is generally aseismic, and is unrelated to rifting. There is a positive aeromagnetic intensity anomaly over the central Bangweulu depression, and there is also a magnetic anomaly density high over the central part of the BB, surrounded by a concentric low [6]. A roughly circular anomaly, outlined by the -140 mgal contour, of the regional Bouguer gravity field is centerd on Lake Bangweulu

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

The Catholic School in Zambia 1964-2014: Catholic and Catholic?

ERIC Educational Resources Information Center

Carmody, Brendan

2015-01-01

This article sketches the history of the Catholic school in Zambia over a 50-year period noting how for reasons of political acceptability it increasingly became less at home with its religious mission thereby finding itself with an unclear sense of purpose. In order to redeem its identity, this article argues that there is need for the school to…

Aflatoxin contamination of groundnut and maize in Zambia: observed and potential concentrations

USDA-ARS?s Scientific Manuscript database

Maize and groundnut, important staples in Zambia, are susceptible to aflatoxin-producing fungi. Aflatoxins are potent human carcinogens also associated with stunting and immunosuppression. Although health and economic burdens of aflatoxins are well known, patterns of contamination in maize and grou...

Investment Incentives and the Implementation of the Framework Convention on Tobacco Control: Evidence from Zambia

PubMed Central

Drope, Jeffrey; Labonte, Ronald; Zulu, Richard; Goma, Fastone

2016-01-01

Purpose Policy misalignment across different sectors of government serves as one of the pivotal barriers to WHO Framework convention on Tobacco Control (FCTC) implementation. This paper examines the logic used by government officials to justify providing investment incentives to increase tobacco processing and manufacturing in the context of FCTC implementation in Zambia. Methods We conducted qualitative semi-structured interviews with key informants from government, civil society and intergovernmental economic organizations (n=23). We supplemented the interview data with an analysis of public documents pertaining to economic development policy in Zambia. Results We found gross misalignments between the policies of the economic sector and efforts to implement the provisions of the FCTC. Our interviews uncovered the rationale used by officials in the economic sector to justify providing economic incentives to bolster tobacco processing and manufacturing in Zambia: 1) tobacco is not consumed by Zambians/tobacco is an export commodity, 2) economic benefits outweigh health costs, and 3) tobacco consumption is a personal choice. Conclusions Much of the struggle Zambia has experienced implementing the FCTC can be attributed to misalignments between the economic and health sectors. Zambia’s development agenda seeks to bolster agricultural processing and manufacturing. Tobacco control proponents must understand and work within this context of economic development in order to foster productive strategies with those working on tobacco supply issues. These findings are broadly applicable to the global analysis on the barriers and facilitators of FCTC implementation. It is important that the Ministry of Health monitors the tobacco policy of other sectors and engages with these sectors to find ways of harmonizing FCTC implementation across sectors. PMID:26135987

Outbreak of Plague in a High Malaria Endemic Region - Nyimba District, Zambia, March-May 2015.

PubMed

Sinyange, Nyambe; Kumar, Ramya; Inambao, Akatama; Moonde, Loveness; Chama, Jonathan; Banda, Mapopa; Tembo, Elliot; Nsonga, Beron; Mwaba, John; Fwoloshi, Sombo; Musokotwane, Kebby; Chizema, Elizabeth; Kapin'a, Muzala; Hang'ombe, Benard Mudenda; Baggett, Henry C; Hachaambwa, Lottie

2016-08-12

Outbreaks of plague have been recognized in Zambia since 1917 (1). On April 10, 2015, Zambia's Ministry of Health was notified by the Eastern Provincial Medical Office of possible bubonic plague cases in Nyimba District. Eleven patients with acute fever and cervical lymphadenopathy had been evaluated at two rural health centers during March 28-April 9, 2015; three patients died. To confirm the outbreak and develop control measures, the Zambia Ministry of Health's Field Epidemiology Training Program (ZFETP) conducted epidemiologic and laboratory investigations in partnership with the University of Zambia's schools of Medicine and Veterinary Medicine and the provincial and district medical offices. Twenty-one patients with clinically compatible plague were identified, with symptom onset during March 26-May 5, 2015. The median age was 8 years, and all patients were from the same village. Blood specimens or lymph node aspirates from six (29%) patients tested positive for Yersinia pestis by polymerase chain reaction (PCR). There is an urgent need to improve early identification and treatment of plague cases. PCR is a potential complementary tool for identifying plague, especially in areas with limited microbiologic capacity. Twelve (57%) patients, including all six with PCR-positive plague and all three who died, also tested positive for malaria by rapid diagnostic test (RDT). Plague patients coinfected with malaria might be misdiagnosed as solely having malaria, and appropriate antibacterial treatment to combat plague might not be given, increasing risk for mortality. Because patients with malaria might be coinfected with other pathogens, broad spectrum antibiotic treatment to cover other pathogens is recommended for all children with severe malaria, until a bacterial infection is excluded.

Bayesian Decision Tree for the Classification of the Mode of Motion in Single-Molecule Trajectories

PubMed Central

Türkcan, Silvan; Masson, Jean-Baptiste

2013-01-01

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens -toxin (CPT) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CPT trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to

Distance to Care, Facility Delivery and Early Neonatal Mortality in Malawi and Zambia

PubMed Central

Lohela, Terhi J.; Campbell, Oona M. R.; Gabrysch, Sabine

2012-01-01

Background Globally, approximately 3 million babies die annually within their first month. Access to adequate care at birth is needed to reduce newborn as well as maternal deaths. We explore the influence of distance to delivery care and of level of care on early neonatal mortality in rural Zambia and Malawi, the influence of distance (and level of care) on facility delivery, and the influence of facility delivery on early neonatal mortality. Methods and Findings National Health Facility Censuses were used to classify the level of obstetric care for 1131 Zambian and 446 Malawian delivery facilities. Straight-line distances to facilities were calculated for 3771 newborns in the 2007 Zambia DHS and 8842 newborns in the 2004 Malawi DHS. There was no association between distance to care and early neonatal mortality in Malawi (OR 0.97, 95%CI 0.58–1.60), while in Zambia, further distance (per 10 km) was associated with lower mortality (OR 0.55, 95%CI 0.35–0.87). The level of care provided in the closest facility showed no association with early neonatal mortality in either Malawi (OR 1.02, 95%CI 0.90–1.16) or Zambia (OR 1.02, 95%CI 0.82–1.26). In both countries, distance to care was strongly associated with facility use for delivery (Malawi: OR 0.35 per 10km, 95%CI 0.26–0.46). All results are adjusted for available confounders. Early neonatal mortality did not differ by frequency of facility delivery in the community. Conclusions While better geographic access and higher level of care were associated with more frequent facility delivery, there was no association with lower early neonatal mortality. This could be due to low quality of care for newborns at health facilities, but differential underreporting of early neonatal deaths in the DHS is an alternative explanation. Improved data sources are needed to monitor progress in the provision of obstetric and newborn care and its impact on mortality. PMID:23300599

Flexible engineering designs for urban water management in Lusaka, Zambia.

PubMed

Tembo, Lucy; Pathirana, Assela; van der Steen, Peter; Zevenbergen, Chris

2015-01-01

Urban water systems are often designed using deterministic single values as design parameters. Subsequently the different design alternatives are compared using a discounted cash flow analysis that assumes that all parameters remain as-predicted for the entire project period. In reality the future is unknown and at best a possible range of values for design parameters can be estimated. A Monte Carlo simulation could then be used to calculate the expected Net Present Value of project alternatives, as well as so-called target curves (cumulative frequency distribution of possible Net Present Values). The same analysis could be done after flexibilities were incorporated in the design, either by using decision rules to decide about the moment of capacity increase, or by buying Real Options (in this case land) to cater for potential capacity increases in the future. This procedure was applied to a sanitation and wastewater treatment case in Lusaka, Zambia. It included various combinations of on-site anaerobic baffled reactors and off-site waste stabilisation ponds. For the case study, it was found that the expected net value of wastewater treatment systems can be increased by 35-60% by designing a small flexible system with Real Options, rather than a large inflexible system.

The development and evaluation of content validity of the Zambia Spina Bifida Functional Measure: Preliminary studies

PubMed Central

Amosun, Seyi L.; Shilalukey-Ngoma, Mary P.; Kafaar, Zuhayr

2017-01-01

Background Very little is known on outcome measures for children with spina bifida (SB) in Zambia. If rehabilitation professionals managing children with SB in Zambia and other parts of sub-Saharan Africa are to instigate measuring outcomes routinely, a tool has to be made available. The main objective of this study was to develop an appropriate and culturally sensitive instrument for evaluating the impact of the interventions on children with SB in Zambia. Methods A mixed design method was used for the study. Domains were identified retrospectively and confirmation was done through a systematic review study. Items were generated through semi-structured interviews and focus group discussions. Qualitative data were downloaded, translated into English, transcribed verbatim and presented. These were then placed into categories of the main domains of care deductively through the process of manifest content analysis. Descriptive statistics, alpha coefficient and index of content validity were calculated using SPSS. Results Self-care, mobility and social function were identified as main domains, while participation and communication were sub-domains. A total of 100 statements were generated and 78 items were selected deductively. An alpha coefficient of 0.98 was computed and experts judged the items. Conclusions The new functional measure with an acceptable level of content validity titled Zambia Spina Bifida Functional Measure (ZSBFM) was developed. It was designed to evaluate effectiveness of interventions given to children with SB from the age of 6 months to 5 years. Psychometric properties of reliability and construct validity were tested and are reported in another study. PMID:28951850

Tobacco Use and Secondhand Smoke Exposure During Pregnancy in Two African Countries: Zambia and the Democratic Republic of the Congo

PubMed Central

Chomba, Elwyn; Tshefu, Antoinette; Onyamboko, Marie; Kaseba - Sata, Christine; Moore, Janet; McClure, Elizabeth M; Moss, Nancy; Goco, Norman; Bloch, Michele; Goldenberg, Robert L

2013-01-01

Objective To study pregnant women’s knowledge, attitudes and behaviors towards tobacco use and secondhand smoke (SHS) exposure, and exposure to advertising for and against tobacco products in Zambia and the Democratic Republic of the Congo (DRC). Design Prospective cross-sectional survey between November 2004 and September 2005. Setting Antenatal care clinics in Lusaka, Zambia and Kinshasa, DRC. Population Pregnant women in Zambia (909) and the DRC (847). Methods Research staff administered a structured questionnaire to pregnant women attending antenatal care clinics. Main Outcome Measures Pregnant women’s use of tobacco, exposure to SHS, knowledge of the harms of tobacco, and exposure to advertising for and against tobacco products. Results Only about 10% of pregnant women reported having ever tried cigarettes (6.6% Zambia; 14.1% DRC). However, in the DRC, 41.8% of pregnant women had ever tried other forms of tobacco, primarily snuff. About 10% of pregnant women and young children were frequently or always exposed to SHS. Pregnant women’s knowledge of the hazards of smoking and SHS exposure was extremely limited. About 13% of pregnant women had seen or heard advertising for tobacco products in the last 30 days. Conclusions Tobacco use and SHS exposure pose serious threats to the health of women, infants, and children. In many African countries, maternal and infant health outcomes are often poor and will likely worsen if maternal tobacco use increases. Our findings suggest that a “window of opportunity” exists to prevent increased tobacco use and SHS exposure of pregnant women in Zambia and the DRC. PMID:20230310

Movement Behaviour of Traditionally Managed Cattle in the Eastern Province of Zambia Captured Using Two-Dimensional Motion Sensors.

PubMed

Lubaba, Caesar H; Hidano, Arata; Welburn, Susan C; Revie, Crawford W; Eisler, Mark C

2015-01-01

Two-dimensional motion sensors use electronic accelerometers to record the lying, standing and walking activity of cattle. Movement behaviour data collected automatically using these sensors over prolonged periods of time could be of use to stakeholders making management and disease control decisions in rural sub-Saharan Africa leading to potential improvements in animal health and production. Motion sensors were used in this study with the aim of monitoring and quantifying the movement behaviour of traditionally managed Angoni cattle in Petauke District in the Eastern Province of Zambia. This study was designed to assess whether motion sensors were suitable for use on traditionally managed cattle in two veterinary camps in Petauke District in the Eastern Province of Zambia. In each veterinary camp, twenty cattle were selected for study. Each animal had a motion sensor placed on its hind leg to continuously measure and record its movement behaviour over a two week period. Analysing the sensor data using principal components analysis (PCA) revealed that the majority of variability in behaviour among studied cattle could be attributed to their behaviour at night and in the morning. The behaviour at night was markedly different between veterinary camps; while differences in the morning appeared to reflect varying behaviour across all animals. The study results validate the use of such motion sensors in the chosen setting and highlight the importance of appropriate data summarisation techniques to adequately describe and compare animal movement behaviours if association to other factors, such as location, breed or health status are to be assessed.

Diabetes mellitus, hypertension and albuminuria in rural Zambia: a hospital-based survey.

PubMed

Rasmussen, Jon B; Thomsen, Jakúp A; Rossing, Peter; Parkinson, Shelagh; Christensen, Dirk L; Bygbjerg, Ib C

2013-09-01

To assess albuminuria in rural Zambia among patients with diabetes mellitus only (DM group), hypertension only (HTN group) and patients with combined DM and HTN (DM/HTN group). A cross-sectional survey was conducted at St. Francis Hospital in the Eastern province of Zambia. Albumin-creatinine ratio in one urine sample was used to assess albuminuria. Other information obtained included age, sex, body mass index (BMI), waist circumference (WC), blood pressure (BP), glycosylated haemoglobin (HbA1c ), random capillary glucose, time since diagnosis, medication and family history of DM or HTN. A total of 193 participants were included (DM group: n = 33; HTN group: n = 92; DM/HTN group: n = 68). The participants in the DM group used insulin more frequently as diabetes medication than the DM/HTN group (P < 0.05). Furthermore, the DM group was younger and had lower BMI, WC and BP than the two other groups. In the DM group, HTN group and DM/HTN group, microalbuminuria was found in 12.1%, 19.6% and 29.4% (P = 0.11), and macroalbuminuria was found in 0.0%, 3.3% and 13.2% (P = 0.014), respectively. The urine albumin (P = 0.014) and albumin-creatinine ratio (P = 0.0006) differed between the three groups. This hospital-based survey in rural Zambia found a lower frequency of albuminuria among the participants than in previous studies of patients with DM or HTN in urban sub-Saharan Africa. © 2013 John Wiley & Sons Ltd.

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

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.

Insecticide resistance and role in malaria transmission of Anopheles funestus populations from Zambia and Zimbabwe.

PubMed

Choi, Kwang S; Christian, Riann; Nardini, Luisa; Wood, Oliver R; Agubuzo, Eunice; Muleba, Mbanga; Munyati, Shungu; Makuwaza, Aramu; Koekemoer, Lizette L; Brooke, Basil D; Hunt, Richard H; Coetzee, Maureen

2014-10-08

Two mitochondrial DNA clades have been described in Anopheles funestus populations from southern Africa. Clade I is common across the continent while clade II is known only from Mozambique and Madagascar. The specific biological status of these clades is at present unknown. We investigated the possible role that each clade might play in the transmission of Plasmodium falciparum and the insecticide resistance status of An. funestus from Zimbabwe and Zambia. Mosquitoes were collected inside houses from Nchelenge District, Zambia and Honde Valley, Zimbabwe in 2013 and 2014. WHO susceptibility tests, synergist assays and resistance intensity tests were conducted on wild females and progeny of wild females. ELISA was used to detect Plasmodium falciparum circumsporozoite protein. Specimens were identified to species and mtDNA clades using standard molecular methods. The Zimbabwean samples were all clade I while the Zambian population comprised 80% clade I and 20% clade II in both years of collection. ELISA tests gave an overall infection rate of 2.3% and 2.1% in 2013, and 3.5% and 9.2% in 2014 for Zimbabwe and Zambia respectively. No significant difference was observed between the clades. All populations were resistant to pyrethroids and carbamates but susceptible to organochlorines and organophosphates. Synergist assays indicated that pyrethroid resistance is mediated by cytochrome P450 mono-oxygenases. Resistance intensity tests showed high survival rates after 8-hrs continuous exposure to pyrethroids but exposure to bendiocarb gave the same results as the susceptible control. This is the first record of An. funestus mtDNA clade II occurring in Zambia. No evidence was found to suggest that the clades are markers of biologically separate populations. The ability of An. funestus to withstand prolonged exposure to pyrethroids has serious implications for the use of these insecticides, either through LLINs or IRS, in southern Africa in general and resistance management

Provisioning of Game Meat to Rural Communities as a Benefit of Sport Hunting in Zambia

PubMed Central

White, Paula A.; Belant, Jerrold L.

2015-01-01

Sport hunting has reportedly multiple benefits to economies and local communities; however, few of these benefits have been quantified. As part of their lease agreements with the Zambia Wildlife Authority, sport hunting operators in Zambia are required to provide annually to local communities free of charge i.e., provision a percentage of the meat obtained through sport hunting. We characterized provisioning of game meat to rural communities by the sport hunting industry in Zambia for three game management areas (GMAs) during 2004–2011. Rural communities located within GMAs where sport hunting occurred received on average > 6,000 kgs per GMA of fresh game meat annually from hunting operators. To assess hunting industry compliance, we also compared the amount of meat expected as per the lease agreements versus observed amounts of meat provisioned from three GMAs during 2007–2009. In seven of eight annual comparisons of these GMAs, provisioning of meat exceeded what was required in the lease agreements. Provisioning occurred throughout the hunting season and peaked during the end of the dry season (September–October) coincident with when rural Zambians are most likely to encounter food shortages. We extrapolated our results across all GMAs and estimated 129,771 kgs of fresh game meat provisioned annually by the sport hunting industry to rural communities in Zambia at an approximate value for the meat alone of >US$600,000 exclusive of distribution costs. During the hunting moratorium (2013–2014), this supply of meat has halted, likely adversely affecting rural communities previously reliant on this food source. Proposed alternatives to sport hunting should consider protein provisioning in addition to other benefits (e.g., employment, community pledges, anti-poaching funds) that rural Zambian communities receive from the sport hunting industry. PMID:25693191

Provisioning of game meat to rural communities as a benefit of sport hunting in Zambia.

PubMed

White, Paula A; Belant, Jerrold L

2015-01-01

Sport hunting has reportedly multiple benefits to economies and local communities; however, few of these benefits have been quantified. As part of their lease agreements with the Zambia Wildlife Authority, sport hunting operators in Zambia are required to provide annually to local communities free of charge i.e., provision a percentage of the meat obtained through sport hunting. We characterized provisioning of game meat to rural communities by the sport hunting industry in Zambia for three game management areas (GMAs) during 2004-2011. Rural communities located within GMAs where sport hunting occurred received on average > 6,000 kgs per GMA of fresh game meat annually from hunting operators. To assess hunting industry compliance, we also compared the amount of meat expected as per the lease agreements versus observed amounts of meat provisioned from three GMAs during 2007-2009. In seven of eight annual comparisons of these GMAs, provisioning of meat exceeded what was required in the lease agreements. Provisioning occurred throughout the hunting season and peaked during the end of the dry season (September-October) coincident with when rural Zambians are most likely to encounter food shortages. We extrapolated our results across all GMAs and estimated 129,771 kgs of fresh game meat provisioned annually by the sport hunting industry to rural communities in Zambia at an approximate value for the meat alone of >US$600,000 exclusive of distribution costs. During the hunting moratorium (2013-2014), this supply of meat has halted, likely adversely affecting rural communities previously reliant on this food source. Proposed alternatives to sport hunting should consider protein provisioning in addition to other benefits (e.g., employment, community pledges, anti-poaching funds) that rural Zambian communities receive from the sport hunting industry.

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…

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.

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.

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

Lead intoxicated children in Kabwe, Zambia.

PubMed

Bose-O'Reilly, Stephan; Yabe, John; Makumba, Joseph; Schutzmeier, Paul; Ericson, Bret; Caravanos, Jack

2017-10-28

Kabwe is a lead contaminated mining town in Zambia. Kabwe has extensive lead contaminated soil and children in Kabwe ingest and inhale high quantities of this toxic dust. The aim of this paper is to analyze the health impact of this exposure for children. Health data from three existing studies were re-analyzed. Over 95% of children living in the most affected townships had high blood lead levels (BLLs) > 10µg/dL. Approximately 50% of those children had BLLs ≥ 45µg/dL. The existing data clearly establishes the presence of a severe environmental health crisis in Kabwe which warrants immediate attention. Copyright © 2017 Elsevier Inc. All rights reserved.

Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models.

PubMed

Liang, Shih-Hsiung; Walther, Bruno Andreas; Shieh, Bao-Sen

2017-01-01

Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables associated with reproduction. Our

Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models

PubMed Central

Liang, Shih-Hsiung; Walther, Bruno Andreas

2017-01-01

Background Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. Methods We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. Results The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables

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.

Improving Allocation And Management Of The Health Workforce In Zambia.

PubMed

Walsh, Fiona J; Musonda, Mutinta; Mwila, Jere; Prust, Margaret Lippitt; Vosburg, Kathryn Bradford; Fink, Günther; Berman, Peter; Rockers, Peter C

2017-05-01

Building a health workforce in low-income countries requires a focused investment of time and resources, and ministries of health need tools to create staffing plans and prioritize spending on staff for overburdened health facilities. In Zambia a demand-based workload model was developed to calculate the number of health workers required to meet demands for essential health services and inform a rational and optimized strategy for deploying new public-sector staff members to the country's health facilities. Between 2009 and 2011 Zambia applied this optimized deployment policy, allocating new health workers to areas with the greatest demand for services. The country increased its health worker staffing in districts with fewer than one health worker per 1,000 people by 25.2 percent, adding 949 health workers to facilities that faced severe staffing shortages. At facilities that had had low staffing levels, adding a skilled provider was associated with an additional 103 outpatient consultations per quarter. Policy makers in resource-limited countries should consider using strategic approaches to identifying and deploying a rational distribution of health workers to provide the greatest coverage of health services to their populations. Project HOPE—The People-to-People Health Foundation, Inc.

Evaluation of service quality in family planning clinics in Lusaka, Zambia.

PubMed

Hancock, Nancy L; Vwalika, Bellington; Sitali, Elizabeth Siyama; Mbwili-Muleya, Clara; Chi, Benjamin H; Stuart, Gretchen S

2015-10-01

To determine the quality of contraceptive services in family planning clinics in Lusaka, Zambia, using a standardized approach. We utilized the Quick Investigation of Quality, a cross-sectional survey tool consisting of a facility assessment, client-provider observation and client exit interview, in public-sector family planning clinics. Data were collected on availability of seven contraceptive methods, information given to clients, interpersonal relations between providers and clients, providers' technical competence and mechanisms for continuity and follow-up. Data were collected from five client-provider observations and client exit interviews in each of six public-sector family planning clinics. All clinics had at least two contraceptive methods continuously available for the preceding 6 months. Most providers asked clients about concerns with their contraceptive method (80%) and told clients when to return to the clinic (87%). Most clients reported that the provider advised what to do if a problem develops (93%), described possible side effects (89%), explained how to use the method effectively (85%) and told them when to come for follow-up (83%). Clients were satisfied with services received (93%). This application of the Quick Investigation of Quality showed that the participating family planning clinics in Lusaka, Zambia, were prepared to offer high-quality services with the available commodities and that clients were satisfied with the received services. Despite the subjective client satisfaction, quality improvement efforts are needed to increase contraceptive availability. Although clients perceived the quality of care received to be high, family planning service quality could be improved to continuously offer the full spectrum of contraceptive options. The Quick Investigation of Quality was easily implemented in Lusaka, Zambia, and this simple approach could be utilized in a variety of settings as a modality for quality improvement. Copyright © 2015

Targeting indoor residual spraying for malaria using epidemiological data: a case study of the Zambia experience.

PubMed

Pinchoff, Jessie; Larsen, David A; Renn, Silvia; Pollard, Derek; Fornadel, Christen; Maire, Mark; Sikaala, Chadwick; Sinyangwe, Chomba; Winters, Benjamin; Bridges, Daniel J; Winters, Anna M

2016-01-06

In Zambia and other sub-Saharan African countries affected by ongoing malaria transmission, indoor residual spraying (IRS) for malaria prevention has typically been implemented over large areas, e.g., district-wide, and targeted to peri-urban areas. However, there is a recent shift in some countries, including Zambia, towards the adoption of a more strategic and targeted IRS approach, in coordination with increased emphasis on universal coverage of long-lasting insecticidal nets (LLINs) and effective insecticide resistance management. A true targeted approach would deliver IRS to sub-district areas identified as high-risk, with the goal of maximizing the prevention of malaria cases and deaths. Together with the Government of the Republic of Zambia, a new methodology was developed applying geographic information systems and satellite imagery to support a targeted IRS campaign during the 2014 spray season using health management information system data. This case study focuses on the developed methodology while also highlighting the significant research gaps which must be filled to guide countries on the most effective strategy for IRS targeting in the context of universal LLIN coverage and evolving insecticide resistance.

Lusaka, Zambia during SAFARI-2000: A Collection Point for Ozone Pollution

NASA Technical Reports Server (NTRS)

Thompson, Anne M.; Witte, Jacquelyn C.; Freiman, M. Tal; Phahlane, N. Agnes; Coetzee, G. J. R.; Bhartia, P. K. (Technical Monitor)

2002-01-01

In August and September, throughout south central Africa, seasonal clearing of dry vegetation and other fire-related activities lead to intense smoke haze and ozone formation. The first ozone soundings in the heart of the southern African burning region were taken at Lusaka, Zambia (155 deg S, 28 deg E) in early September 2000. Over 90 ppbv ozone was recorded at the surface (1.3 km elevation) and column tropospheric ozone was greater than 50 DU during a stagnant period. These values are much higher than concurrent measurements over Nairobi (1 deg S, 38 deg E) and Irene (25 deg S, 28 deg E, near Pretoria). The heaviest ozone pollution layer (800-500 hPa) over Lusaka is due to recirculated trans-boundary ozone. Starting out over Zambia, Angola, and Namibia, ozone heads east to the Indian Ocean, before turning back over Mozambique and Zimbabwe, heading toward Lusaka. Thus, Lusaka is a collection point for pollution, consistent with a picture of absolutely stable layers recirculating in a gyre over southern Africa.

Increased Economic Relations Between China and Zambia in the Last Decade: Implications on Zambia’s Existing Bilateral Relations with the United States

DTIC Science & Technology

2013-12-13

Pacific region, but also the world at large. China and the U.S. have agreed to a new model of relations, based on practical cooperation and...as a significant model to determine whether the increase in China and Zambia relations lead to a change in the nature of bilateral relations between...a model in countries with similar features and given the circumstances. The last decade has seen China step up its economic activities on the

Identification of Some Zeolite Group Minerals by Application of Artificial Neural Network and Decision Tree Algorithm Based on SEM-EDS Data

NASA Astrophysics Data System (ADS)

Akkaş, Efe; Evren Çubukçu, H.; Akin, Lutfiye; Erkut, Volkan; Yurdakul, Yasin; Karayigit, Ali Ihsan

2016-04-01

Identification of zeolite group minerals is complicated due to their similar chemical formulas and habits. Although the morphologies of various zeolite crystals can be recognized under Scanning Electron Microscope (SEM), it is relatively more challenging and problematic process to identify zeolites using their mineral chemical data. SEMs integrated with energy dispersive X-ray spectrometers (EDS) provide fast and reliable chemical data of minerals. However, considering elemental similarities of characteristic chemical formulae of zeolite species (e.g. Clinoptilolite ((Na,K,Ca)2 -3Al3(Al,Si)2Si13O3612H2O) and Erionite ((Na2,K2,Ca)2Al4Si14O36ṡ15H2O)) EDS data alone does not seem to be sufficient for correct identification. Furthermore, the physical properties of the specimen (e.g. roughness, electrical conductivity) and the applied analytical conditions (e.g. accelerating voltage, beam current, spot size) of the SEM-EDS should be uniform in order to obtain reliable elemental results of minerals having high alkali (Na, K) and H2O (approx. %14-18) contents. This study which was funded by The Scientific and Technological Research Council of Turkey (TUBITAK Project No: 113Y439), aims to construct a database as large as possible for various zeolite minerals and to develop a general prediction model for the identification of zeolite minerals using SEM-EDS data. For this purpose, an artificial neural network and rule based decision tree algorithm were employed. Throughout the analyses, a total of 1850 chemical data were collected from four distinct zeolite species, (Clinoptilolite-Heulandite, Erionite, Analcime and Mordenite) observed in various rocks (e.g. coals, pyroclastics). In order to obtain a representative training data set for each minerals, a selection procedure for reference mineral analyses was applied. During the selection procedure, SEM based crystal morphology data, XRD spectra and re-calculated cationic distribution, obtained by EDS have been used for the

Floodwaters Renew Zambia's Kafue Wetland

NASA Technical Reports Server (NTRS)

2004-01-01

Not all floods are unwanted. Heavy rainfall in southern Africa between December 2003 and April 2004 provided central Zambia with floodwaters needed to support the diverse uses of water within the Kafue Flats area. The Kafue Flats are home to about one million people and provide a rich inland fishery, habitat for an array of unique wildlife, and the means for hydroelectricity production. The Flats falls between two dams: Upstream to the west (not visible here) is the Izhi-tezhi, and downstream (middle right of the images) is the Kafue Gorge dam. Since the construction of these dams, the flooded area has been reduced and the timing and intensity of the inundation has changed. During June 2004 an agreement was made with the hydroelectricity company to restore water releases from the dams according to a more natural flooding regime. These images from NASA's Multi-angle Imaging SpectroRadiometer (MISR) illustrate surface changes to the wetlands and other surfaces in central Zambia resulting from an unusually lengthy wet season. The Kafue Flats appear relatively dry on July 19, 2003 (upper images), with the Kafue River visible as a slender dark line that snakes from east to west on its way to join the Zambezi (visible in the lower right-hand corner). On July 21, 2004 (lower images), well into the dry season, much of the 6,500-square kilometer area of the Kafue Flats remains inundated. To the east of the Kafue Flats is Lusaka, the Zambian capital, visible as a pale area in the middle right of the picture, north of the river. In the upper portions of these images is the prominent roundish shape of the Lukanga Swamp, another important wetland.

The images along the left are natural-color views from MISR's nadir camera, and the images along the right are angular composites in which red band data from MISR's 46o forward, nadir, and 46o backward viewing cameras is displayed as red, green and blue, respectively. In order to preserve brightness variations among the various

Antimicrobial Resistant Enteropathogenic Escherichia coli and Salmonella spp. in Houseflies Infesting Fish in Food Markets in Zambia.

PubMed

Songe, Mwansa M; Hang'ombe, Bernard M; Knight-Jones, Theodore J D; Grace, Delia

2016-12-28

Diarrhea is one of the most common diseases and is a leading cause of death in developing countries. This is often caused by contaminated food. Poor food hygiene standards are exacerbated by the presence of flies which can transmit a variety of infectious microorganisms, particularly through animal source foods. This fact becomes especially important in developing countries like Zambia, where fish is a highly valued source of protein. Our interest in this study was to identify if the flies that beset food markets in Zambia carry important pathogenic bacteria on their bodies, and subsequently if these bacteria carry resistance genes to commonly used antibiotics, which would indicate problems in eradicating these pathogens. The present study took into account fish vendors' and consumers' perception of flies and interest in interventions to reduce their numbers. We conducted semi-structured interviews with (1) traders (comprised of randomly selected males and females) and (2) consumers (including randomly selected males and females). Thereafter, we collected flies found on fish in markets in Mongu and Lusaka districts of Zambia. For the entire study, a total of 418 fly samples were analyzed in the laboratory and Salmonella spp. and enteropathogenic Escherichia coli were isolated from the flies. Further laboratory screening revealed that overall, 17.2% (72/418) (95% CI; 43.2%-65.5%) of total samples analyzed contained Extended-Spectrum Beta-Lactamase (ESBL)-producing E. coli . These significant findings call for a strengthening of the antibiotic administering policy in Zambia and the development of sustainable interventions to reduce fly numbers in food markets and improve food safety and hygiene.

Application Of Decision Tree Approach To Student Selection Model- A Case Study

NASA Astrophysics Data System (ADS)

Harwati; Sudiya, Amby

2016-01-01

The main purpose of the institution is to provide quality education to the students and to improve the quality of managerial decisions. One of the ways to improve the quality of students is to arrange the selection of new students with a more selective. This research takes the case in the selection of new students at Islamic University of Indonesia, Yogyakarta, Indonesia. One of the university's selection is through filtering administrative selection based on the records of prospective students at the high school without paper testing. Currently, that kind of selection does not yet has a standard model and criteria. Selection is only done by comparing candidate application file, so the subjectivity of assessment is very possible to happen because of the lack standard criteria that can differentiate the quality of students from one another. By applying data mining techniques classification, can be built a model selection for new students which includes criteria to certain standards such as the area of origin, the status of the school, the average value and so on. These criteria are determined by using rules that appear based on the classification of the academic achievement (GPA) of the students in previous years who entered the university through the same way. The decision tree method with C4.5 algorithm is used here. The results show that students are given priority for admission is that meet the following criteria: came from the island of Java, public school, majoring in science, an average value above 75, and have at least one achievement during their study in high school.

Increased fairness in priority setting processes within the health sector: the case of Kapiri-Mposhi District, Zambia

PubMed Central

2014-01-01

Background The challenge of priority setting (PS) in health care within contexts of severe resource limitations has continued to receive attention. Accountability for Reasonableness (AFR) has emerged as a useful framework to guide the implementation of PS processes. In 2006, the AFR approach to enhance legitimate and fair PS was introduced by researchers and decision makers within the health sector in the EU funded research project entitled ‘Response to Accountable priority setting for Trust in health systems’ (REACT). The project aimed to strengthen fairness and accountability in the PS processes of health systems at district level in Zambia, Tanzania and Kenya. This paper focuses on local perceptions and practices of fair PS (baseline study) as well as at the evolution of such perceptions and practices in PS following an AFR based intervention (evaluation study), carried out at district level in Kapiri-Mposhi District in Zambia. Methods Data was collected using in depth interviews (IDIs), focus group discussions (FGDs) and review of documents from national to district level. The study population for this paper consisted of health related stakeholders employed in the district administration, in non-governmental organizations (NGO) and in health facilities. Results During the baseline study, concepts of legitimacy and fairness in PS processes were found to be grounded in local values of equity and impartiality. Government and other organizational strategies strongly supported devolution of PS and decision making procedures. However, important gaps were identified in terms of experiences of stakeholder involvement and fairness in PS processes in practice. The evaluation study revealed that a transformation of the views and methods regarding fairness in PS processes was ongoing in the study district, which was partly attributed to the AFR based intervention. Conclusions The study findings suggest that increased attention was given to fairness in PS processes at

Increased fairness in priority setting processes within the health sector: the case of Kapiri-Mposhi District, Zambia.

PubMed

Zulu, Joseph M; Michelo, Charles; Msoni, Carol; Hurtig, Anna-Karin; Byskov, Jens; Blystad, Astrid

2014-02-18

The challenge of priority setting (PS) in health care within contexts of severe resource limitations has continued to receive attention. Accountability for Reasonableness (AFR) has emerged as a useful framework to guide the implementation of PS processes. In 2006, the AFR approach to enhance legitimate and fair PS was introduced by researchers and decision makers within the health sector in the EU funded research project entitled 'Response to Accountable priority setting for Trust in health systems' (REACT). The project aimed to strengthen fairness and accountability in the PS processes of health systems at district level in Zambia, Tanzania and Kenya. This paper focuses on local perceptions and practices of fair PS (baseline study) as well as at the evolution of such perceptions and practices in PS following an AFR based intervention (evaluation study), carried out at district level in Kapiri-Mposhi District in Zambia. Data was collected using in depth interviews (IDIs), focus group discussions (FGDs) and review of documents from national to district level. The study population for this paper consisted of health related stakeholders employed in the district administration, in non-governmental organizations (NGO) and in health facilities. During the baseline study, concepts of legitimacy and fairness in PS processes were found to be grounded in local values of equity and impartiality. Government and other organizational strategies strongly supported devolution of PS and decision making procedures. However, important gaps were identified in terms of experiences of stakeholder involvement and fairness in PS processes in practice. The evaluation study revealed that a transformation of the views and methods regarding fairness in PS processes was ongoing in the study district, which was partly attributed to the AFR based intervention. The study findings suggest that increased attention was given to fairness in PS processes at district level. The changes were linked to a

Construction and validation of a decision tree for treating metabolic acidosis in calves with neonatal diarrhea.

PubMed

Trefz, Florian M; Lorch, Annette; Feist, Melanie; Sauter-Louis, Carola; Lorenz, Ingrid

2012-12-06

The aim of the present prospective study was to investigate whether a decision tree based on basic clinical signs could be used to determine the treatment of metabolic acidosis in calves successfully without expensive laboratory equipment. A total of 121 calves with a diagnosis of neonatal diarrhea admitted to a veterinary teaching hospital were included in the study. The dosages of sodium bicarbonate administered followed simple guidelines based on the results of a previous retrospective analysis. Calves that were neither dehydrated nor assumed to be acidemic received an oral electrolyte solution. In cases in which intravenous correction of acidosis and/or dehydration was deemed necessary, the provided amount of sodium bicarbonate ranged from 250 to 750 mmol (depending on alterations in posture) and infusion volumes from 1 to 6.25 liters (depending on the degree of dehydration). Individual body weights of calves were disregarded. During the 24 hour study period the investigator was blinded to all laboratory findings. After being lifted, many calves were able to stand despite base excess levels below -20 mmol/l. Especially in those calves, metabolic acidosis was undercorrected with the provided amount of 500 mmol sodium bicarbonate, which was intended for calves standing insecurely. In 13 calves metabolic acidosis was not treated successfully as defined by an expected treatment failure or a measured base excess value below -5 mmol/l. By contrast, 24 hours after the initiation of therapy, a metabolic alkalosis was present in 55 calves (base excess levels above +5 mmol/l). However, the clinical status was not affected significantly by the metabolic alkalosis. Assuming re-evaluation of the calf after 24 hours, the tested decision tree can be recommended for the use in field practice with minor modifications. Calves that stand insecurely and are not able to correct their position if pushed require higher doses of sodium bicarbonate, if there is clinical evidence of a

Responding to non-communicable diseases in Zambia: a policy analysis.

PubMed

Mukanu, Mulenga M; Zulu, Joseph Mumba; Mweemba, Chrispin; Mutale, Wilbroad

2017-04-24

Non-communicable diseases (NCDs) are an emerging global health concern. Reports have shown that, in Zambia, NCDs are also an emerging problem and the government has begun initiating a policy response. The present study explores the policy response to NCDs by the Ministry of Health in Zambia using the policy triangle framework of Walt and Gilson. A qualitative approach was used for the study. Data collected through key informant interviews with stakeholders who were involved in the NCD health policy development process as well as review of key planning and policy documents were analysed using thematic analysis. The government's policy response was as a result of international strategies from WHO, evidence of increasing disease burden from NCDs and pressure from interest groups. The government developed the NCD strategic plan based on the WHO Global Action Plan for NCDs 2013-2030. Development of the NCD strategic plan was driven by the government through the Ministry of Health, who set the agenda and adopted the final document. Stakeholders participated in the fine tuning of the draft document from the Ministry of Health. The policy development process was lengthy and this affected consistency in composition of the stakeholders and policy development momentum. Lack of representative research evidence for some prioritised NCDs and use of generic targets and indicators resulted in the NCD strategic plan being inadequate for the Zambian context. The interventions in the strategic plan also underutilised the potential of preventing NCDs through health education. Recent government pronouncements were also seen to be conflicting the risk factor reduction strategies outlined in the NCD strategic plan. The content of the NCD strategic plan inadequately covered all the major NCDs in Zambia. Although contextual factors like international strategies and commitments are crucial catalysts to policy development, there is need for domestication of international guidelines and

Inquiry-Based Science Education: A Scenario on Zambia's High School Science Curriculum

ERIC Educational Resources Information Center

Chabalengula, Vivien M.; Mumba, Frackson

2012-01-01

This paper is aimed at elucidating the current state of inquiry-based science education (IBSE) in Zambia's high school science curriculum. Therefore, we investigated Zambian teachers' conceptions of inquiry; determined inquiry levels in the national high school science curriculum materials, which include syllabi, textbooks and practical exams; and…

Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree.

PubMed

Acharya, Tri Dev; Lee, Dong Ha; Yang, In Tae; Lee, Jae Kang

2016-07-12

Water bodies are essential to humans and other forms of life. Identification of water bodies can be useful in various ways, including estimation of water availability, demarcation of flooded regions, change detection, and so on. In past decades, Landsat satellite sensors have been used for land use classification and water body identification. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed by Landsat 8 has improved, enabling better characterization of land cover and increased data size. Therefore, it is necessary to explore the most appropriate and practical water identification methods that take advantage of the improved image quality and use the fewest inputs based on the original OLI bands. The objective of the study is to explore the potential of a J48 decision tree (JDT) in identifying water bodies using reflectance bands from Landsat 8 OLI imagery. J48 is an open-source decision tree. The test site for the study is in the Northern Han River Basin, which is located in Gangwon province, Korea. Training data with individual bands were used to develop the JDT model and later applied to the whole study area. The performance of the model was statistically analysed using the kappa statistic and area under the curve (AUC). The results were compared with five other known water identification methods using a confusion matrix and related statistics. Almost all the methods showed high accuracy, and the JDT was successfully applied to the OLI image using only four bands, where the new additional deep blue band of OLI was found to have the third highest information gain. Thus, the JDT can be a good method for water body identification based on images with improved resolution and increased size.

Can Classification Tree Analyses Help Improve Decision Making About Treatments for Depression and Anxiety Disorders? A Preliminary Investigation

PubMed Central

Rhodes, Louisa; Naumann, Ulrike M.

2011-01-01

Objective: To identify how decisions about treatment are being made in secondary services for anxiety disorders and depression and, specifically, whether it was possible to predict the decisions to refer for evidence-based treatments. Method: Post hoc classification tree analysis was performed using a sample from an audit on implementation of the National Institute for Health and Clinical Excellence Guidelines for Depression and Anxiety Disorders. The audit was of 5 teams offering secondary care services; they included psychiatrists, psychologists, community psychiatric nurses, social workers, dual-diagnosis workers, and vocational workers. The patient sample included all of those with a primary problem of depression (n = 56) or an anxiety disorder (n = 16) who were offered treatment from February 16 to April 3, 2009. The outcome variable was whether or not evidence-based treatments were offered, and the predictor variables were presenting problem, risk, comorbid problem, social problems, and previous psychiatric history. Results: Treatment decisions could be more accurately predicted for anxiety disorders (93% correct) than for depression (55%). For anxiety disorders, the presence or absence of social problems was a good predictor for whether evidence-based or non–evidence-based treatments were offered; 44% (4/9) of those with social problems vs 100% (6/6) of those without social problems were offered evidence-based treatments. For depression, patients’ risk rating had the largest impact on treatment decisions, although no one variable could be identified as individually predictive of all treatment decisions. Conclusions: Treatment decisions were generally consistent for anxiety disorders but more idiosyncratic for depression, making the development of a decision-making model very difficult for depression. The lack of clarity of some terms in the clinical guidelines and the more complex nature of depression could be factors contributing to this difficulty

Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition.

PubMed

Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi

2014-01-01

In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm (GA) weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Information Provision in Emergency Settings: The Experience of Refugee Communities in Zambia

ERIC Educational Resources Information Center

Kanyengo, Brendah Kakulwa; Kanyengo, Christine Wamunyima

2011-01-01

This article identifies information provision services in emergency settings using Zambia as a case study by identifying innovative ways of providing library and information services. The thrust of the article is to analyze information management practices of organizations that work within refugee camps and how they take specific cognizance of the…

Linguistic Landscapes and the Sociolinguistics of Language Vitality in Multilingual Contexts of Zambia

ERIC Educational Resources Information Center

Banda, Felix; Jimaima, Hambaba

2017-01-01

The article illustrates a sociolinguistics of language vitality that accounts for "minority" and unofficial languages across multiple localities in dispersed communities of multilingual speakers of Zambia where only seven out of seventy-three indigenous languages have been designated official and "zoned" for use in specified…

Promotion of couples' voluntary HIV counseling and testing: a comparison of influence networks in Rwanda and Zambia.

PubMed

Kelley, April L; Hagaman, Ashley K; Wall, Kristin M; Karita, Etienne; Kilembe, William; Bayingana, Roger; Tichacek, Amanda; Kautzman, Michele; Allen, Susan A

2016-08-08

Many African adults do not know that partners in steady or cohabiting relationships can have different HIV test results. Despite WHO recommendations for couples' voluntary counseling and testing (CVCT), fewer than 10 % of couples have been jointly tested and counseled. We examine the roles and interactions of influential network leaders (INLs) and influential network agents (INAs) in promoting CVCT in Kigali, Rwanda and Lusaka, Zambia. INLs were identified in the faith-based, non-governmental, private, and health sectors. Each INL recruited and mentored several INAs who promoted CVCT. INLs and INAs were interviewed about demographic characteristics, promotional efforts, and working relationships. We also surveyed CVCT clients about sources of CVCT information. In Zambia, 53 INAs and 31 INLs were surveyed. In Rwanda, 33 INAs and 27 INLs were surveyed. Most (75 %-90 %) INAs believed that INL support was necessary for their promotional work. Zambian INLs reported being more engaged with their INAs than Rwandan INLs, with 58 % of Zambian INLs reporting that they gave a lot of support to their INAs versus 39 % in Rwanda. INAs in both Rwanda and Zambia reported promoting CVCT via group forums (77 %-97 %) and speaking to a community leader about CVCT (79 %-88 %) in the past month. More Rwandan INAs and INLs reported previous joint or individual HIV testing compared with their Zambian counterparts, of which more than half had not been tested. In Zambia and Rwanda, 1271 and 3895 CVCT clients were surveyed, respectively. Hearing about CVCT from INAs during one-on-one promotions was the most frequent source of information reported by clients in Zambia (71 %). In contrast, Rwandan couples who tested were more likely to have heard about CVCT from a previously tested couple (59 %). CVCT has long been endorsed for HIV prevention but few couples have been reached. Influential social networks can successfully promote evidence-based HIV prevention in Africa. Support from

Antimicrobial Resistant Enteropathogenic Escherichia coli and Salmonella spp. in Houseflies Infesting Fish in Food Markets in Zambia

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