An improved support vector machines model in medical data analysis

Abstract

The support vector machine (SVM) technique is an emerging classification scheme that has been successfully employed in solving many classification problems. However, three main traits: features selection, dimension reduction and parameters selection, essentially influence the classification performance of SVM models. Therefore, this study developed an improved support vector machine (IMSVM) model using factor analysis (FA), kernel sliced inverse regression (KSIR) and honey-bee mating optimisation with genetic algorithms (HBMOG) to deal with feature selection, dimension reduction, and parameter selection issues, respectively, for SVM models. Then, the statlog heart dataset from the Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI) was used to demonstrate the performance of the IMSVM model. Experimental results revealed that the IMSVM model can provide more accurate classification results than the results obtained by classification models in previous literature. Thus, the proposed model is a promising alternative for analysing medical data.