Effective Heart Attack Prediction method using Machine Learning Algorithm

In today’s era development cardiovascular disease is the most common disease that cause a heart attack. So, diagnosing heart disease patients on time is the most challenging task in the medical field. Due to expensive treatment the poor people prohibit to take over heart disease treatment. The cardiologist’s doctors generally predict the patient’s heart attack with the help of a scrutinized patient report, electrocardiography, blood tests, computer report based on the patient’s previous database before seriousness of patient diseases. This study aims to analyze different machine learning (ML) algorithms that are currently available for predicting heart attacks, including decision trees, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression. Finally, we obtained the best algorithm from this existing work based on accuracy, precision and recall.