MACHINE LEARNING-BASED HEART DISEASE PREDICTION SYSTEM

Abstract

One of the main reasons for unexpected death or the main cause of mortality globally is heart syndrome. A person now loses their life due to heart disease every minute, making heart disease deaths one of the greatest challenges in today’s society. Heart function is impacted by heart syndrome. According to a survey conducted by the World Health Organization, heart disease claimed the lives of 18 million individuals. The paucity of resources makes early disease prediction extremely difficult. When used in the healthcare industry, machine learning has the potential to diagnose diseases or syndromes accurately and early. Medical parameter attributes must be present in datasets. The datasets are analyzed in Python using the Random Forest Machine Learning Algorithm. In this study, a trustworthy machine learning algorithm is used to forecast cardiac illness or syndrome. An algorithm reads a CSV file of patient record data that it has obtained from surveys or hospitals. Following dataset reading, the procedure is carried out, and the effective heart attack level or syndrome level is generated.