Feature selection for the SVM: An application to hypertension diagnosis

A support vector machine (SVM) is a novel classifier based on the statistical learning theory. To increase the performance of classification, the approach of SVM with kernel is usually used in classification tasks. In this study, we first attempted to investigate the performance of SVM with kernel. Several kernel functions, polynomial, RBF, summation, and multiplication were employed in the SVM and the feature selection approach developed [Hermes, L., & Buhmann, J. M. (2000). Feature selection for support vector machines. In Proceedings of the international conference on pattern recognition (ICPR’00) (Vol. 2, pp. 716–719)] was utilized to determine the important features. Then, a hypertension diagnosis case was implemented and 13 anthropometrical factors related to hypertension were selected. Implementation results show that the performance of combined kernel approach is better than the single kernel approach. Compared with backpropagation neural network method, SVM based method was found to have a better performance based on two epidemiological indices such as sensitivity and specificity.