Abstract

Heart disease prediction has become one of the serious issues in INDIA particularly in ANDHRA PRADESH. Managing heart isn't a simple errand in light. According to the statistics, the passing pace of individuals is 6 out of 10 who are being diseased and effected by coronary illness. The fact is that, occasionally specialists do flop in foreseeing the illness and diagnosing it since an extremely uncommon number of the frameworks anticipating heart illnesses dependent upon a few attributes like: age, family ancestry, diabetes, hypertension, cholesterol, tobacco chewing, smoking, liquor consumption, weight or physical dormancy, and so on. To improve the prediction system, a framework has been proposed which can anticipate the heart disease effectively by considering some extra attributes such as self-potential slope curve, self-potential depression level, and chest pain. The primary point is to locate the best arrangement utilizing the past informational indexes and check whether the individual have a heart issue or not. Classification algorithms – Decision tree, Naïve bays theorem, support vector machine, logistic regression are implemented and by combining the result prediction is done.