Predicting Heart Attacks with Machine Learning Algorithms

The heart, a vital organ in humans, plays a crucial role in purifying and circulating blood throughout the body. Heart attacks, a leading cause of death worldwide, manifest through symptoms like chest pain, rapid heartbeat, and breathing difficulties. This study presents a comprehensive examination of heart attacks and contemporary prediction techniques. It provides an overview of different machine learning methodologies employed for heart attack prediction, including Decision Tree, Logistic Regression, Support Vector Machines (SVM), Naive Bayes, Random Forest, K-Nearest Neighbours (KNN), and XGBoost Classifier. The comparison of these algorithms is conducted based on their respective features, offering insights into their effectiveness. The purpose of this study is to support ongoing efforts to create precise and effective models of prediction for cardiac diagnosis and prevention.