Classification of prostate cancer patients and healthy individuals by means of a hybrid algorithm combing SVM and evolutionary algorithms

Abstract

This research presents a new hybrid algorithm able to select a set of features that makes it possible to classify healthy individuals and those affected by prostate cancer. In this research the feature selection is performed with the help of evolutionary algorithms. This kind of algorithms, have proven in previous researches their ability for obtaining solutions for optimization problems in very different fields. In this study, a hybrid algorithm based on evolutionary methods and support vector machine is developed for the selection of optimal feature subsets for the classification of data sets. The results of the algorithm using a reduced data set demonstrates the performance of the method when compared with non-hybrid methodologies.