Prediction of heart disease using Modified Hybrid Classifier

Cardiovascular diseases leading to heart attack kills nearly 17.9 million humans. The complexity of this disease’s lies in the fact that it suddenly fails functioning human and then SOP (Standard Operating Plan) is required; if not provided on time, patients’ life danger. Proper health care system takes time detect cause effectively start diagnosis whereas our proposed efficiently accurately tells client weather he has a disease or also whether patient will face such kind near future not. developed based machine learning techniques as Naive Bayes, XGBoost gradient classifier, decision tree support vector (SVM). We have selected some external factors which may lead future. Furthermore, integrated Web application been alert gives user-friendly interface for recognition prediction. analyzed 13 diagnostic 5 environmental factors. Stalogand Cleveland dataset are combinedly used article. This suggested achieved good accuracy compared previous methods earlier. In addition can easily be implemented public domain spread awareness regarding possibility identified.