• Aadarsh Singh
• Rohini Bhosle

Every day, cases of heart disease rise faster and are very important, and we are concerned that such diseases are predicted in advance. This diagnosis is a difficult task. It must be performed accurately and efficiently. This project report focuses on the potential for heart disease by training six machine learning algorithms. Using data from the Kaggle website, you can analyze and compare logistic regression models, Naive Bayes, K-Nearest Neighbors, support vector machine, decision trees, and random forests. The most robust model that determines the key features of the model. Using a very useful approach, we adjusted the methods of improving the accuracy of heart attack prediction for each individual using the model. The strength of the proposed model is satisfactory and using decision trees and random forests that showed good accuracy compared to previously used classifiers such as Naive Bayes, to provide a specific individual. We were able to demonstrate that heart disease was used.

Naive bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, Machine Learning and Logistic Regression.

"Heart Disease Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.c161-c164, March-2025, Available :http://www.jetir.org/papers/JETIR2503220.pdf

"Heart Disease Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppc161-c164, March-2025, Available at : http://www.jetir.org/papers/JETIR2503220.pdf