Heart Disease Detection Using Machine Learning Techniques

Abstract

According to a recent World Health Organization survey, 17.9 million people die each year as a result of heart-related disorders, and the number is continuously growing. With a rising population and sickness, it is becoming more difficult to diagnose disease and provide pr oper therapy at the appropriate time. However, there is a ray of optimism in that recent technological breakthroughs have expedited the public health sector by generating sophisticated functional biomedical solutions.The purpose of this paper is to examine the different data mining techniques, namely Naive Bayes, Random Forest Classification, Decision Tree, and Support Vector Machine, using a qualified dataset for Heart disease prediction, which includes attributes such as gender, age, chest pain type, blood pressure, blood sugar, and so on. The research entails determining the correlations between the different qualities of the dataset using typical data mining techniques and then using the attributes to forecast the likelihood of a cardiac condition. These machine learning algorithms need less time to forecast disease with more precision, reducing the loss of priceless lives all across the world.