Heart Disease Prediction Using Classification Models

Abstract

A lot of people die all over the world due to various cardiovascular diseases. These diseases cause serious threat to the life of people and mostly affect people in the age range of 25-69years. There are various conventional methods and manual medical procedures of diagnosis for detection of cardiovascular diseases. However, these procedural methods are highly expensive and have reduced scalability performance, and hence are highly time consuming and sometimes prone to errors. Therefore, it is important to detect cardiovascular diseases using certain intelligent systems which deployMachine Learning (ML)techniques for better feature extraction, and early detection to avoid critical health circumstances, and thereby assist in improving medical diagnosis. This paper proposes and implements a cost effective and affordable ML model, which can detect whether a given person suffers from heart disease or not, given their clinical record, at an early stage. The proposed model is a composition of various ML classifiers such as K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Logistic Regression(LR), Support Vector Machine(SVM) and Naïve Bayes(NB), on theCleveland dataset and makes a comparison amongst them. The model deploys data preprocessing such as normalization, feature extraction and classification. In this paper it is observed that the Naïve Bayes classifier gives best prediction which is 86.88%compared to all other classifiers and the Decision Tree classifier gives the least performance amongst all, which is 78.69%.