International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Prediction of Heart Condition through Machine Learning Algorithms |
|---|---|
| ABSTRACT | Heart disease stands as one of the leading causes of morbidity and mortality across the globe, presenting an urgent challenge for modern healthcare systems. Early detection and proactive diagnosis are critical factors in lowering mortality rates and designing personalized patient treatment workflows. Traditional clinical methodologies frequently depend on extensive diagnostic tests and time-consuming manual expert assessments, which may be less accessible or late in resource-limited environments. This study presents an automated, highly reliable Heart Disease Prediction System utilizing state-of-the-art machine learning paradigms, specifically comparing baseline models against optimized ensemble techniques. We systematically preprocess clinical data, manage multicollinearity through regularization, and evaluate four primary predictive models: Logistic Regression (with L1/L2 penalties), Random Forest, XGBoost, and LightGBM. Comprehensive hyperparameter tuning and early stopping strategies are deployed to prevent overfitting and guarantee robust clinical generalization. The empirical results demonstrate that gradient-boosted models achieve superior performance profiles, yielding an optimal balance of precision and clinical recall, thereby establishing a high-utility auxiliary diagnostic framework for clinical professionals. |
| AUTHOR | DEEPA P., G. CHIDAMBARAM, A. SUDHAKAR P.G. Student, Department of Computer Science and Engineering, Bharathidasan Engineering College, Nattrampalli, Tamil Nadu, India Assistant Professor & Supervisor, Department of Computer Science and Engineering, Bharathidasan Engineering College, Nattrampalli, Tamil Nadu, India Head of the Department, Department of Computer Science and Engineering, Bharathidasan Engineering College, Nattrampalli, Tamil Nadu, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406053 |
| pdf/53_Prediction of Heart Condition through Machine Learning Algorithms.pdf | |
| KEYWORDS | |
| References | 1. A. S. Arya, R. M. Mohammad, et al., "Cardiovascular Disease Prediction Using Advanced Data Mining Protocols," IEEE Transactions on Biomedical Engineering, vol. 65, no. 4, pp. 812–820, 2021. 2. T. K. Santhanam and S. B. Prakash, "A Comparative Evaluation of Boosting Paradigms in Tabular Electronic Health Records," International Journal of Medical Informatics, vol. 114, pp. 45–53, 2022. 3. J. C. Boosting, "XGBoost: A Scalable Tree Boosting System," Proceedings of the 22nd ACM SIGKDD International Conference, pp. 785–794, 2016. 4. G. Ke, Q. Meng, et al., "LightGBM: A Highly Efficient Light Gradient Boosting Machine," Advances in Neural Information Processing Systems, pp. 3146–3154, 2017. 5. P. M. Deepa, "Prediction of Heart Condition Through Machine Learning Algorithms," Master of Engineering Phase II Dissertation, Anna University, Chennai, 2026. |
