Heart disease prediction using machine learning techniques

Abstract

Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a heart disease or not. A lot of people suffer from cardiovascular diseases (CVDs), which even cost people their lives all around the world. Machine learning can be used to detect whether a person is suffering from a cardiovascular disease by considering certain attributes like chest pain, cholesterol level, age of the person and some other attributes. Classification algorithms based on supervised learning which is a type of machine learning can make diagnoses of cardiovascular diseases easy. Algorithms like K-Nearest Neighbor (KNN), Random Forest are used to classify people who have a heart disease from people who do not. Two supervised machine learning algorithms are used in this paper which are, K-Nearest Neighbor (K-NN) and Random Forest. The prediction accuracy obtained by K-Nearest Neighbor (K-NN) is 86.885% and the prediction accuracy obtained by Random Forest algorithm is 81.967%.