Abstract
In this chapter, we report a comparison study of seven nonparametric classifiers (multilayer perceptron neural networks, radial basis function neural networks, support vector machines, CART, CHAID and QUEST classification trees, and random forests) as compared to linear discriminant analysis, quadratic discriminant analysis and logistic regression tested in a real data application of mild cognitive impaired elderly patients conversion to dementia. When classification results are compared both on overall accuracy, specificity and sensitivity, linear discriminant analysis and random forests rank first among all the classifiers.
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