Optimization Heart Disease Prediction using Machine Learning Models

Abstract

Healthcare is currently one of the most pressing global issues, with an increase in the incidence of cardiac disease affecting all age groups, particularly the young. Rapid identification and treatment of heart problems can potentially save lives. Artificial intelligence has the potential to significantly aid in this effort. In this study, we aimed to develop a heart disease prediction model using machine learning techniques. We utilized several models, including Support Vector Machine (SVM), K-Neighbors Classifier, Random Forest Classifier, Decision Tree, and Logistic Regression. Based on our experiments, the logistic regression and K-NN models produced the best results, with accuracies of 0.95592% and 0.956194%, respectively. Our findings suggest that machine learning models can be optimized for heart disease prediction and have the potential to improve healthcare outcomes.