Abstract

Heart disease is now days one of the primary basis of death in world. Prediction of heart diseases requires more precision, perfection and correctness because a little mistake can cause death of the person and also it associates with many risky factors. To deal with the problem there is essential need of prediction system to get accurate and reliable about diseases. Machine learning provides the way to predict any kind of event which take training from natural events. In this paper we have implemented supervise machine learning classification algorithms like Logistic Regression, K-Nearest Neighbour, SVM, Decision Tree, and Random Forest and calculate accuracy by using existing dataset from the Cleveland database of UCI repository of heart disease patients

Description