Heart Attack Prediction using Machine Learning Algorithms

Abstract

One of the most crucial parts of the human body is the heart. When the heart's blood supply is cut off, a heart attack happens. The most frequent cause of a blockage is the buildup of fats, cholesterol, and other substances inside the coronary arteries that provide blood to the heart (which eventually results in plaque growth). This study sought to identify which anthropometric characteristics had a high likelihood of having an impact on a person having a heart attack in order to design a program for heart attack analysis using machine learning algorithms. The researchers were able to acquire data from a variety of sources to identify the variables that may be used to create the output in order to fulfill the study's objectives. The existing heart attack analysis was reviewed by the researchers. They discovered that the researchers' algorithms were hardly noteworthy. Another ongoing study uses a variety of data mining and machine learning approaches to analyze vast volumes of patient data in an effort to predict heart attacks before they happen, ultimately helping professionals and the healthcare industry. The software for Heart Attack Prediction was created by taking into account various data from previous studies.