Comparative Analysis of Random Forest, SVM, and Naive Bayes for Cardiovascular Disease Prediction - Rayadhani, Windy Aldora; Rahardi, Majid (Journal of Applied Informatics and Computing, 2025)
Cardiovascular disease is one of the leading causes of death worldwide; therefore, accurate early detection is essential to reduce fatal risks. This study aims to compare the performance of three machine learning algorithms — Random Forest, Support Vector Machine (SVM), and Naïve Bayes — in predicting cardiovascular disease risk using the Mendeley Cardiovascular Disease Dataset, which contains 1,000 patient records and 14 clinical attributes. The models were evaluated using accuracy, precision, recall, and F1-score metrics, and their performance differences were statistically tested using the paired t-test. The experimental results indicate that the Random Forest algorithm achieved the best performance with 99% accuracy, 100% recall, 98% precision, and an F1-score of 99%. The SVM model followed with 98% accuracy and 100% recall, while the Naïve Bayes algorithm obtained 94.5% accuracy and an F1-score of 95%. The p-value < 0.05 confirmed that the performance differences among the three models were statistically significant. From a clinical perspective, a model with high recall, such as Random Forest, is more desirable because it reduces the likelihood of false negatives, which are critical in heart disease diagnosis. The feature importance analysis also revealed that age, resting blood pressure, and cholesterol level were the most influential factors in predicting cardiovascular risk. These findings suggest that machine learning algorithms, particularly Random Forest, have strong potential to be implemented in Clinical Decision Support Systems (CDSS) for accurate and efficient early detection of cardiovascular disease.