An optimized feature selection based on genetic approach and support vector machine for heart disease

Heart disease diagnosis is found to be a challenging issue which can offer a computerized estimate about the level of heart disease so that supplementary action can be made easy. Thus, heart disease diagnosis has expected massive attention worldwide among the healthcare environment. Optimization algorithms played a significant role in heart disease diagnosis with good efficiency. The objective of this paper is to propose an optimization function on the basis of support vector machine (SVM). This objective function is used in the genetic algorithm (GA) for selecting the more significant features to get heart disease. The experimental results of the GA–SVM are compared with the various existing feature selection algorithms such as Relief, CFS, Filtered subset, Info gain, Consistency subset, Chi squared, One attribute based, Filtered attribute, Gain ratio, and GA. The receiver operating characteristic analysis is performed to evaluate the good performance of SVM classifier. The proposed framework is demonstrated in the MATLAB environment with a dataset collected from Cleveland heart disease database.