A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine

The aim of this study is to search the efficiency of binary particle swarm optimization (BPSO) and genetic algorithm (GA) techniques as feature selection models on determination of coronary artery disease (CAD) existence based upon exercise stress testing (EST) data. Also, increasing the classification performance of the classifier is another aim. The dataset having 23 features was obtained from patients who had performed EST and coronary angiography. Support vector machine (SVM) with k-fold cross-validation method is used as the classifier system of CAD existence in both BPSO and GA feature selection techniques. Classification results of feature selection technique using BPSO and GA are compared with each other and also with the results of the whole features using simple SVM model. The results show that feature selection technique using BPSO is more successful than feature selection technique using GA on determining CAD. Also with the new dataset composed by feature selection technique using BPSO, this study reached more accurate values of success on CAD existence research with more little complexity of classifier system and more little classification time compared with whole features used SVM.