Analysis of Machine Learning Techniques for Prediction of Cardiac Diseases

Early detection and diagnosis are crucial because cardiovascular diseases are the leading cause of fatalities in the world. Timely diagnosis and intervention can significantly improve patient outcomes and reduce healthcare costs. In this study, we used Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbour, and Support Vector Machines to predict the risk of cardiovascular diseases based on two datasets obtained from the UCI library. We assessed each algorithm’s performance based on its accuracy, precision, recall, and F1 Score. Our findings show that KNN performed better than the other algorithms, with Accuracy of 90.16%. This study shows how machine learning algorithms can be used to predict cardiac diseases and offers guidance for further study in this area.