Hybrid Machine Learning Techniques for Heart Disease Prediction

Abstract

Diseases can affect people both physically and mentally, as contracting and living with a disease can alter the affected person's perspective on life. A disease that affects the parts of an organism, which isn't because of any immediate external injury. Diseases are often known to be medical conditions that are related to specific symptoms and signs. The deadliest diseases in humans are arteria coronaria disease (blood flow obstruction), followed by cerebrovascular disease and lower respiratory infections. Heart disease are most unpredictable and unexpectable. We can able to predict the heart disease using machine learning technique. The datasets are taken from UCI repository which is a public dataset. These trained dataset are used for the prediction. Techniques like Decision tree, Support Vector Machine, K Nearest Neighbor and Random Forest algorithms are used in the prediction of heart disease and hybrid of these algorithms provides 94 % accuracy.