• Mr. Soumen Sen
• Vivek Kumar
• Goutam Kumar Rajak
• Subha Sundar Chakraborty

The application of machine learning algorithms in predicting heart attacks has become a crucial area of investigation in modern healthcare. This study delves into how computer applications can analyze patient data to pinpoint individuals at potential risk for coronary events. By assessing variables such as weight, blood pressure, and medical history, these algorithms demonstrate notable capabilities, with some studies reporting accuracy rates exceeding 90%. Although these systems aid healthcare providers in recognizing high-risk patients, they do not offer a conclusive diagnosis of heart attacks. Thus, the role of medical professionals remains vital, as they collaborate with researchers to refine and improve these predictive tools. The incorporation of machine learning shows great promise for the early detection of those prone to heart attacks, facilitating timely interventions to reduce cardiovascular risks. The utilization of machine learning algorithms in predicting heart attacks has emerged as a significant area of research in contemporary healthcare. This paper explores the role of computer applications in analyzing patient data to identify individuals who may be at risk of experiencing a coronary event.

Medical statistics evaluation, coronary heart attack threat evaluation, coronary heart disease prediction, and coronary heart attack prediction the use of Machine Learning (ML) and Support Vector Machines (SVM), Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), and so own.

"Utilizing Machine Learning Algorithms for Heart Attack Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.i1-i13, June-2024, Available :http://www.jetir.org/papers/JETIR2406801.pdf

"Utilizing Machine Learning Algorithms for Heart Attack Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppi1-i13, June-2024, Available at : http://www.jetir.org/papers/JETIR2406801.pdf