Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Abstract

A myocardial infarction, indigestion, or even death can take place as a result of several illnesses known as heart disease, including restricted or blocked veins. Depending on the extent of the patient's side effects, the condition is anticipated by the supervised classification classifier. This research intends to investigate how Machine Learning Tree Classifiers depict Heart Disease Prediction. Pattern recognition tree classifiers are analyzed using Random Forest, Decision Tree, Logistic Regression, Support Vector Machine (SVM), and K-nearest Neighbors (KNN) based on their correctness and AUC Gryphon scores. With an execution time of 1.32 seconds, better precision of 85%, and a Coefficient Of determination (r score of 0.8739, the Random Forest machine learning classification surpassed its effectiveness in this investigation of coronary heart disease detection.