• Mr.Ashish Digambar Sawant
• Dr. M.S.Ali

Machine learning, powered by vast healthcare data, helps diagnose heart diseases early, saving lives. This research investigates the use of machine learning approaches for accurate prediction of heart diseases. Heart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions.[1] Coronary heart disease (CHD) /Acute coronary syndrome (ACS).[2] The mortality rate is expanding due to obesity, cholesterol, high blood pressure and usage of tobacco among the people.[3] An accurate prediction of heart disease is necessary for the early stage of treatment and overcoming the mortality rate.[4] Machine learning (ML) can be used to assist clinical decision-making. We developed a ML model for the prediction of 1-year mortality after heart transplantation (HT) in adults with congenital heart disease.[5] This study proposes a machine learning approach to Prediction of heart disease more accurately. Moreover, Accuracy, F1-Measure, Precision, and Recall are used to measure the performance of machine learning models.[4]Different types of supervised machine learning model can be applied on data set.[6] We wanted to design and develop machine learning model for Prediction of heart disease. In addition, a user-friendly web app and a user-friendly mobile app are built based on the most accurate model.[4]

Coronary heart disease (CHD), Machine learning (ML), supervised machine learning model, Accuracy, F1-Measure, Precision, and Recall etc.

"Review: Machine learning approach for heart disease prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.a553-a562, March-2024, Available :http://www.jetir.org/papers/JETIR2403075.pdf

"Review: Machine learning approach for heart disease prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppa553-a562, March-2024, Available at : http://www.jetir.org/papers/JETIR2403075.pdf