Novel application of a statistical technique, Random Forests, in a bacterial source tracking study

Smith, Amanda; Sterba-Boatwright, Blair; Mott, Joanna

doi:10.1016/j.watres.2010.05.019

Cited by 68 publications

(40 citation statements)

References 26 publications

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“…Equivalent or even superior performances have been reported for Linear Discriminant Analysis and Random Forests when compared with Neural Networks, Classification Trees and Support Vector Machines [see e.g. [34,47,58,67,68]]. However, controversy still prevails regarding the effects on classifiers' performance of different combinations of predictors, data assumptions, sample sizes and parameters tuning [16,17,31,58,69,70].…”

Section: Discussionmentioning

confidence: 99%

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

et al. 2011

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BackgroundDementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test.ResultsPress' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5.ConclusionsWhen taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

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Section: Discussionmentioning

confidence: 99%

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

et al. 2011

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“…Furthermore, there have been studies with fairly small samples in which new classification methods such as RF and NN have been applied with high accuracy [8,13,28]. Some studies have reported equivalent or even superior performance of LR and LDA in comparison with NN, SVM, RF, and FCM [9,13,20,29,30]. Since the performance of NN and SVM depends on tuning parameters, these parameters were optimally determined by grid search.…”

Section: Discussionmentioning

confidence: 99%

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

Tapak

Mahjub

Hamidi³

et al. 2013

Healthc Inform Res

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ObjectivesDiabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes.MethodsThe data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria.ResultsSupport vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350).ConclusionsThe results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

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“…There are numerous machine learning algorithms, including support vector machines [71], artificial neural networks [72], and random forests [73] that can be trained with known microbial community structures to classify environmental samples of unknown origin. Random forest have successfully been used in the context of source tracking using phenotypic data of E. coli [74] and classifying hosts according to Blautia profiles [70], and its applicability to microbiome data is shown in a recent review by Statnikov et al [75]. The random forest algorithm can handle very large input datasets, tolerate outliers in the input data, and generate an unbiased estimate of the classification error.…”

Section: Host Microbiomes and Specificity Of Commensal Organismsmentioning

confidence: 99%

Discovering new indicators of fecal pollution

McLellan

Eren

2014

Trends in Microbiology

149

106

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Fecal pollution indicators are essential to identify and remediate contamination sources and protect public health. Historically, easily cultured facultative anaerobes such as fecal coliforms, Escherichia coli, or enterococci have been used, but these indicators generally provide no information as to their source. More recently, molecular methods have targeted fecal anaerobes, which are much more abundant in humans and other mammals and some strains appear to be associated with certain host sources. Next-generation sequencing and microbiome studies have created an unprecedented inventory of microbial communities associated with fecal sources, allowing reexamination of which taxonomic groups are best suited as informative indicators. The use of new computational methods, such as oligotyping coupled with well-established machine learning approaches, is providing new insights into patterns of host association. In this review we examine the basis for host-specificity and the rationale for using 16S rRNA gene targets for alternative indicators and highlight two taxonomic groups, Bacteroidales and Lachnospiraceae, which are rich in host-specific bacterial organisms. Finally, we discuss considerations for using alternative indicators for water quality assessments with a particular focus on detecting human sewage sources of contamination.

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Novel application of a statistical technique, Random Forests, in a bacterial source tracking study

Cited by 68 publications

References 26 publications

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

Discovering new indicators of fecal pollution

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