Smart Heart Disease Prediction and Prevention System Using Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802197Keywords:
Heart Disease Prediction, Machine Learning, Clinical Data Analysis, Risk Assessment, Classification Algorithms, Preventive HealthcareAbstract
The increasing prevalence of cardiovascular diseases highlights the importance of early and reliable risk identification. This work presents a data-driven heart disease prediction system that utilises machine learning techniques to analyse clinical and lifestyle parameters. The collected data are systematically cleaned and refined to ensure consistency and relevance before model training. Multiple classification algorithms are implemented and evaluated to examine their predictive performance on medical datasets. Experimental results indicate that appropriate model selection significantly enhances the prediction accuracy and generalisation capability. Key health attributes influencing heart disease risk are also analysed to improve the practical usefulness of the system. The proposed approach serves as a supportive analytical tool for early risk assessment and preventive healthcare decision-making. Overall, the study demonstrates the potential of machine learning in developing scalable and efficient solutions for cardiovascular health monitoring
References
1. R. Detrano, A. Janosi, W. Steinbrunn, and M. Pfisterer, "Utilizing Machine Learning Techniques for the Diagnosis of Coronary Heart Disease," IEEE International Conference on Healthcare Informatics (ICHI), 2020. DOI: 10.1109/ICHI48887.2020.9374382.
2. S. Dua and X. Du, "Predicting Heart Disease Risk Through Machine Learning Approaches," 2021 IEEE International Conference on Artificial Intelligence in Healthcare, pp. 112–118.DOI: 10.1109/ICAIH51222.2021.9456123.
3. K. Polat and S. Güneş, "Heart Disease Classification Using Hybrid Machine Learning Models," 2020 IEEE International Conference on Data Science and Advanced Analytics (DSAA). DOI: 10.1109/DSAA49011.2020.00054.
4. N. Al-Makhadmeh and A. Al-Betar, "A Smart System for Predicting Heart Disease Through Supervised Learning," 2021 IEEE International Conference on Intelligent Systems and Data Science (ISDS). DOI:10.1109/ISDS51488.2021.9467741.
5. D. Tomar and S. Agarwal, "Machine Learning Insights in Healthcare Analytics," 2019 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). DOI: 10.1109/ICCIC46655.2019.9031412.
6. M. Priyadharshini, A. Sathish, and K. Vijayakumar. "Early Detection of Cardiac Risk Utilizing Health Data Mining Techniques." 2021 International Conference on Computing, Communication, and Applied Sciences (ICCCAS). DOI: 10.1109/ICCCAS52487.2021.9398742.
7. T. Arun, S. Maheshwari, and R. Janani. A Comparative Analysis of Machine Learning Techniques for Predicting Heart Disease." 2023 IEEE International Conference on Intelligent Computing and Information Systems (ICICIS). DOI: 10.1109/ICICIS56934.2023.10124578.
8. Prakash, M. Karthikeyan, and D. Revathi,―Predictive Analysis of Cardiovascular Diseases Using Clinical Data Mining,‖ IEEE International Conference on Emerging Trends in Engineering and Technology (ICETET), 2021.
9. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
10. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
11. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
12. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
13. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
14. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
15. Gopinathan, V. R. (2025). Intelligent Workload Scheduling for Telecom Cloud Architecture Using Reinforcement Learning. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13244-13255.
16. Rajasekar, M., (2024). Secure Digital Banking with Federated AI: An AWS Cloud-Based Predictive Analytics Architecture for Financial Risk Intelligence. International Journal of Research and Applied Innovations, 7(3), 10735-10740.
17. Anand, L., Tyagi, R., & Mehta, V. (2024, January). Food recognition using deep learning for recipe and restaurant recommendation. In Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (pp. 269-279). Singapore: Springer Nature Singapore.
18. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
19. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
20. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
21. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
22. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
23. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
24. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
25. K. Sharmila, S. Deepak, and R. Gokul, ―Machine Learning Based Clinical Decision Support System for Heart Disease Diagnosis,‖ International Conference on Smart Systems and Sustainable Computing, 2023.
26. L. Keerthana, N. Sanjay, and P. Madhavan. "Data Driven Approach for Heart Disease Classification Using Predictive Models."2022 International Conference on Advanced Trends in ComputerEngineering(ICATCE). DOI:10.1109/ICATCE54876.2022.9754318.
27. R. Swetha, K. Monisha, and S. Prabhu. "Machine Learning Techniques for Cardiovascular Risk Assessment." 2021 IEEE International Conference on Innovations in Information and Communication Technology (ICI2CT). DOI: 10.1109/ICI2CT51068.2021.9412349.
