A comprehensive comparative analysis of machine learning algorithms in heart disease prediction

Abstract This study investigates advanced machine learning techniques for predicting heart disease, emphasizing the critical role of early diagnosis in cardiovascular diseases (CVDs), which remain among the leading causes of mortality worldwide. Early and accurate detection can substantially reduce mortality rates and improve public health outcomes. In this context, advanced machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), have emerged as powerful tools for analyzing complex patterns in medical data. The performance and accuracy of 13 different machine learning algorithms were evaluated in a binary classification task aimed at distinguishing between healthy individuals and those at high risk for heart disease. The combined dataset consisted of 1328 samples, with 50.08% classified as high-risk patients and 49.92% as healthy individuals, providing a balanced distribution for effective machine learning analysis. The dataset included 14 features, such as age, gender, blood pressure, cholesterol, and other health-related factors. Model performance was assessed using 13 different evaluation metrics, and results were reported as mean (m), standard deviation (SD), and root mean square error (RMSE). Pairwise comparisons of algorithms based on accuracy were performed using Significance Testing Between Models to evaluate statistically significant differences. Additionally, an exploratory data analysis was conducted to assess the influence of individual features on model outputs. The findings indicate that the RF algorithm achieved high accuracy (93.82 ± 1.64%) as well as high sensitivity, while models such as Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Extra Trees Classifier (ETC), and Decision Tree (DT) consistently ranked second and third across evaluation metrics. These results demonstrate that leveraging machine learning techniques enhances diagnostic accuracy, facilitates rapid identification of high-risk individuals, and reduces healthcare costs. Ultimately, this study provides a foundation for developing innovative prediction methods and management strategies for cardiovascular diseases.