Abstract: Cardiovascular illness remains the greatest reason for deaths worldwide and the heart disease prediction at the early stage is significance. In this study, we propose a hybrid heart disease prediction system using evolutionary learning algorithms like cascaded neural network and Genetic algorithm. It is used for heart disease prediction at the early stage utilizing the patients therapeutic record. The results are compared with the known supervised classifier Support Vector Machine (SVM). During classification, 13 attributes are given as input to the CNN classifier to predict the risk of heart illness. The proposed framework can be used as a guide by the doctors to predict the disease in a more productive way. The effectiveness of the classifier is tried utilizing the records gathered from 270 patients. The outcomes demonstrate that the Genetic based CNN classifier can anticipate the probability of patients with coronary illness in a more effective manner.
S. Mohandoss, V. Sai Shanmuga Raja and S.P. Rajagopalan, 2017. A Hybrid Heart Disease Prediction System using Evolutionary Learning Algorithms. Asian Journal of Information Technology, 16: 639-644.