Chimp Optimization Algorithm-Based Feature Selection for Cardiac Image-Based Heart Disease Diagnosis

In this paper, we propose chimp optimization algorithm (ChOA) for selection of feature to increase the classification accuracy of heart disease diagnosis. In this approach, noises contained in the cardiac image are removed using median filter initially. Then, GLCM features are extracted from the cardiac image. Among the extracted features, optimal features are chosen using ChOA algorithm. These selected features taken as input to the classifier. In this approach, support vector neural network (SVNN) is used as classifier. The classifier classifies the image into normal and abnormal. Simulation results depict that the ChOA-based SVNN performs superior than the conventional SVNN, ANN, KNN and SVM in terms of accuracy.