Performance Comparison of Machine Learning Algorithms in Heart Disease Prediction with Enhanced Accuracy through Hyper parameter Tuning

Abstract

     Heart disease, which frequently results from blockage of the coronary arteries, the blood channels that supply the heart with oxygen-rich blood, is still one of the top causes of death globally. Plaque and fatty deposits that accumulate along the arterial walls are the main causes of this blockage, which makes the arteries narrow and limits blood flow. Even with cardiac disorders' seriousness and potentially fatal consequences, early detection remains a significant challenge in the medical field, often due to the complex and subtle nature of early symptoms. This diagnostic difficulty highlights the need for advanced computational tools that can support clinical decision-making. In this context, this study investigates the application of machine learning algorithms to heart disease risk prediction. Five artificial intelligence models—Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), and Naive Bayes (NB)—were tested on a dataset of 1,888 records and 14 attributes. In order to increase data quality and guarantee that models can learn patterns efficiently, preprocessing was used to clean and prepare the data first. This was followed by hyper parameter optimization for the SVM and KNN models. The aim of hyper parameter optimization is to maximize the model's performance on the data. With an accuracy of 96.30%, Random Forest outperformed the other models under evaluation. Decision Tree came in second with 95.59% , SVM with 94.55% ,KNN with 0.9425 and Naive Bayes with 0.6966 . These studies demonstrate how machine learning may be used to identify cardiac illness early on by identifying intricate patterns in data and providing more precise results than conventional techniques.