A REVIEW ON HEART DISEASE PREDICTION USING MACHINE LEARNING

Abstract

Heart assumes huge role in living organisms. Diagnosis and prediction of heart related diseases requires more precision, perfection and correctness because a little mistake can cause fatigue problem or death of the person, there are numerous death cases related to heart and their checking is increasing exponentially step by step. To deal with the problem there is essential need of prediction system for awareness about diseases. Machine learning is the part of Artificial Intelligence (AI). It provides prestigious support in predicting any kind of event which takes training from common events. In this paper, we calculate accuracy of machine learning algorithms for predicting heart disease, for this algorithms are k-nearest neighbor, decision tree, linear regression and support vector machine(SVM) by utilizing UCI repository dataset for training and testing. In this paper, review the accuracy of different machine learning approaches and based on count and conclude what one is best among them.