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Heart Disease Prediction Using Machine Learning

“Rishabh Sahu ,Yash Tripathi, Atul Kr Gautam, Pushkal Shukla”

(Department of Information Technology and Engineering, Inderprastha Engineering college (IPEC))

Abstract— Predicting heart disease is one of the most challenging tasks in medicine in recent years. Today about one person dies from a heart attack every minute. Data science plays an important role in processing large amounts of data in healthcare. Because the prediction of heart disease is a difficult task, it is necessary to complete the forecasting process to avoid the risks associated with it and to warn patients in advance. This article uses the cardio logy dataset available in the uci machine learning repository. Functional planning uses different types of data, such as naive Bayes, decision trees, logistic regression, and random forests, to predict heart disease risk and categorize population risk levels. Therefore, this article conducts a comparative study by analyzing the effectiveness of different learning systems. Experimental results confirmed that therandom forest algorithm achieved th e highest accuracy of 90.16% compared to other ml algorithms.

Keywords— Decision Tree, Naive Bayes, Logistic Regression,

Random Forest, Heart Disease Prediction