Heart Attack Prediction using Machine Learning Techniques
Shubham Mall
Abstract
Heart is vital organ in the circulatory system, which functions as a muscular pump responsible for circulating blood throughout the body and plays crucial role in sustaining life. Nowadays heart disease is increasing as per WHO, nearly 30 million people die every year. The increased incidence of heart attacks in younger people highlights the need for an efficient detection system that can recognize early warning signs and prevent future events. An easy-to-use and trustworthy predictive method for determining the risk of heart disease is required due to the impracticality of frequent and costly diagnostic procedures like ECGs for the general population. To meet this requirement, we suggest developing a program that uses basic indications like age, gender, cholesterol, chest pain type, blood pressure and pulse rate to forecast a person’s risk of heart disease. By utilizing neural networks’ established precision and dependability in machine learning methods, this suggested system seeks to offer a useful and approachable instrument for estimating the likelihood of heart-related illnesses.