Abstract
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.
Highlights
Dementia and cognitive impairment associated with aging are a major medical and social concern
In this paper we evaluated the sensitivity, specificity, overall classification accuracy, area under the ROC and Press’ Q of data mining classifiers like Neural Networks (Multilayer Perceptrons and Radial Basis Networks), Support Vector Machines, Classification Trees and Random Forests as compared to the traditional Linear, Quadratic Discriminant Analysis and Logistic Regression in the prediction of the evolution into dementia of 400 elderly people with Mild Cognitive Impairment
The smallest mean ranks were observed for Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Radial Basis Function (RBF), Classification and Regression Tree (CART) and Quick Unbiased Efficient Statistical Tree (QUEST)
Summary
Dementia and cognitive impairment associated with aging are a major medical and social concern. 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. It would be important to improve the value of neuropsychological tests to predict the progression of MCI patients to dementia This can be achieved at a clinical level by increasing the number of patients with longer clinical follow-ups. Research has been steadily building on the accuracy and efficiency of data mining, with classifiers like Neural Networks (NN), Support Vector Machines (SVM), Classification Trees (CT) and Random Forests (RF) used for medical prediction and classification tasks [13,14,19,20,21,22,23,24,25,26,27]. In medical contexts, sensitivity (the ability to predict the condition when the condition is present), specificity (the ability to predict the absence of the condition when the condition is not present) as well as the classifier discriminant power (as estimated from the area under the Receiver Operating Characteristic (ROC) curve) are key features that must be considered when comparing classifiers and diagnostic methods
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