Data mining based on intelligent systems for decision support systems in healthcare


In this paper we make an extensive study of artificial intelligence (AI) techniques that can be used in decision support systems in healthcare. In particular, we propose variants of ensemble methods (i.e., Rotation Forest and Input Decimated Ensembles) that are based on perturbing features, and we make a wide comparison among the ensemble approaches. We illustrate the power of these techniques by applying our approaches to different healthcare problems. Included in this chapter is extensive background material on the single classifier systems, ensemble methods, and feature transforms used in the experimental section.

Keywords texture descriptors; local binary patterns; local ternary patterns; non uniform patterns; support vector machine

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