Classifier Design with Feature Selection for Heart Disease Classification

Many medical reasons are contributing to the depreciating health issues among the people in the global environment. Health issues related to the heart and various other heart diseases are increasing. It was estimated that cardiovascular diseases cause 17.3 million deaths per year, globally [1]. We need to overcome these upcoming challenges with early detection & prevention of heart diseases and bring forth ways of economical diagnosis. Feature Selection (FS) helps in removal of unwanted, redundant & irrelevant data and helps to increase the performance & lessen computation time to give efficient results. We propose the design of a Genetic Programming (GP) based optimized multiclass (m 2) classifier to classify the patient as having heart disease or not based on a heart disease dataset. The system is designed to provide promising results and aid medical practitioners in their diagnosis.