A new evolutionary support vector machine with application to parkinson’s disease diagnosis

Abstract

In this paper, we present a bacterial foraging optimization (BFO) based support vector machine (SVM) classifier, termed as BFO_SVM, and it is applied successfully to Parkinson’s disease (PD) diagnosis. In the proposed BFO-SVM, the issue of parameter optimization in SVM is tackled using the BFO technique. The effectiveness of BFO-SVM has been rigorously evaluated against the PD Dataset. The experimental results demonstrate that the proposed approach outperforms the other two counterparts via 10-fold cross validation analysis. In addition, compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 96.89%.