Classification and prediction of heart disease risk using data mining techniques of Support Vector Machine and Artificial Neural Network

Classification of coronary Heart Disease can be valuable for the medical practitioners in the event that it is automated with the end goal of quick finding and exact result. Foreseeing the presence of Heart Disease precisely can spare patients living days. The target of this paper is to break down the use of AI devices for order and expectation of heart illness. The work incorporates the classes of Heart Disease utilizing Support Vector Machine (SVM) as well as Artificial Neural Network (ANN). Examination is completed among two strategies on the premise of accuracy and training time. This paper introduces a medical choice backing framework for coronary illness characterization in a sane, purpose, precise and fast manner. The dataset utilized are the Cleveland Heart Database and Statlog Database taken from UCI Machine learning dataset vault. In the proposed system model we arrange the data records into two classes in Support Vector Machine as well as Artificial Neural Network. Also analyze the performance of the both the datasets.