HEART ATTACK PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUE

Cardiovascular disease is currently the leading cause of death in high-income countries and projected to become top global by 2030. In United States, heart disease, including conditions like coronary hypertension, stroke, remains primary death. According American Heart Association's 2019 update, approximately 46% US adults, equivalent 116.4 million people, are estimated have hypertension based on new 2017 Hypertension Clinical Practice Guidelines. The impact cardiovascular profound, with a person dying from CVD every 38 seconds, resulting 2,303 CVD-related deaths per day 2016 data. Stroke, as critical event, accounts for about 389.4 daily, stroke-related occurring 3.70 minutes, according same heart's essential function pumping blood throughout body makes any malfunction potentially fatal, organ failure, brain, within minutes. Contributing factors rising incidence heart-related diseases include unhealthy lifestyle choices, work-related stress, poor dietary habits, posing significant health concern. World Health Organization, 17.7 annually, representing 31% all deaths. This worrisome trend also apparent India, where mortality. To combat increasing rates heart- related diseases, particularly among younger individuals, it vital develop an early detection system stroke symptoms prevent fatalities. project aims analyze datasets utilize feature selection techniques construct high-accuracy model using best-performing machine learning algorithms. By deriving valuable insights data, objective contribute prevention improved management conditions.