Study of RF and SVM Machine Learning Model to Predict Heart Disease
DOI:
https://doi.org/10.61453/joit.v2025no19Keywords:
RF Algorithm, SVM Algorithm, Model Interpretability Tools like SHAP and LIMEAbstract
Heart disease remains one of the leading causes of mortality worldwide, making early and accurate diagnosis essential for preventing severe complications. Recent advancements in machine learning have enabled clinicians to analyze complex patient data more effectively than traditional diagnostic approaches. This study evaluates two widely used machine learning models Random Forest (RF) and Support Vector Machine (SVM) for predicting heart disease using a curated clinical dataset. RF achieved an accuracy of 100%, while SVM achieved 98.87%. The study also integrates SHAP and LIME interpretability tools to provide transparent, clinically meaningful explanations. This combined focus on accuracy and explainability distinguishes the study from existing literature.
References
A. Althaf Alia , M.A. Gunavathie , V. Srinivasan , M. Aruna, R. Chennappan, M. Matheena(2025) Securing electronic health records using blockchain-enabled federated learning for IoT-based smart healthcare, Clinical eHealth, 125-133, https://doi.org/10.1016/j.ceh.2025.04.002
Ghazal, T. M., et al. (2023). Heart disease prediction using machine learning. In 2023 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1–7). https://doi.org/10.1109/IDICAIEI61867.2024.10842908
Joshi, K., Anandaram, H., Reddy, G., & Gupta, A. (2023). Analysis of heart disease prediction using various machine learning techniques. In 2023 International Conference on Device Intelligence, Computing, and Communication Technologies. https://doi.org/10.1109/DICCT56244.2023.10110139
Lakshmi, A., & Devi, R. (2023). Heart disease prediction using enhanced Whale Optimization Algorithm-based feature selection with machine learning techniques. In 12th International Conference on System Model & Advancement in Research Trends (SMART) (pp. 1–7). DOI:10.1109/SMART59791.2023.10428617
Lakshmi, K., & Chitra, K. (2024). Stress Net: Multimodal Stress Detection using ECG and EEG Signals. Journal of Data Science, 2024(59), 1-8. https://doi.org/10.61453/jods.v2024no59
M. Aruna, S. Sukumaran, V. Srinivasan (2022), Hybridization of Fuzzy Label propagation and Local Resultant Evidential Clustering Method for Cancer Detection Volume 70 Issue 9, 34-46, https://doi.org/10.14445/22315381/IJETT-V70I9P204
Nasution, N., Hasan, M. A., & Bakri Nasution, F. (2025). Predicting Heart Disease Using Machine Learning: An Evaluation of Logistic Regression, Random Forest, SVM, and KNN Models on the UCI Heart Disease Dataset. IT Journal Research and Development, 9(2), 140–150. https://doi.org/10.25299/itjrd.2025.17941
Naveen, S., Ravindran, S. K., S., G., & Ameen, S. N. (2023). An effective heart disease prediction framework using Random Forest and logistic regression. In Proceedings of the International Conference. DOI:10.1109/ViTECoN58111.2023.10157078
Prasher, S., Nelson, L., & Hariharan, S. (2023). Evaluation of machine learning algorithms for heart disease prediction in healthcare. In 2023 International Conference on Innovations in Engineering and Technology (ICIET). DOI:10.1109/ICIET57285.2023.10220917
Sen, K., & Verma, B. (2023). Heart disease prediction using a soft voting ensemble of gradient boosting models, Random Forest, and Gaussian Naive Bayes. In 2023 4th International Conference for Emerging Technology (INCET). DOI:10.1109/INCET57972.2023.10170399
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Innovation and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
