Cardiac Image-Based Heart Disease Diagnosis Using Bio-Inspired Optimized Technique for Feature Selection to Enhance Classification Accuracy

Abstract

In this chapter, chimp optimization algorithm (ChOA) a bio-inspired optimized technique are proposed for selection of features 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 are taken as input to the classifier. In this approach, support vector neural network (SVNN) is used as a classifier. The classifier classifies the image into normal and abnormal. Simulation results depict that the ChOA-based SVNN performs better than the conventional SVNN, ANN, KNN, and SVM in terms of accuracy.