Feature Selection using Hybrid Dragonfly Algorithm in a Heart Disease Predication System

Abstract

The heart disease considers as one of the fatal disease in many countries. The main reason is due to the approved methods of diagnostic are not available to the ordinary people. Many studies have been done to handle this case with the use of both methods of soft computing and machine learning. In this study, a hybrid binary dragonfly algorithm and mutual information proposed for feature selection, support vector machine and multilayer perceptron employed for classification. The Statlog dataset used for experiments. Out of a total of 270 instances of patient data, 216 employees for the purpose of practicing, 54 of them used for the purpose of examining. Maximum classification accuracy of 94.44% achieved with support vector machine and 92.59% with multilayer perceptron on features selected with binary dragonfly algorithm, whereas with features obtained from mutual information combined with binary dragonfly (MI_BDA) algorithm support vector machine and multilayer perceptron attained an accuracy of 96.29%. The time algorithm takes reduced from 15.4 with binary dragonfly algorithm to 6.95 seconds with MI_BDA.