Authors : Yash Soni; Akhilesh A. Waoo

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/2tf7puhs

Scribd : https://tinyurl.com/25t8hex8

DOI : https://doi.org/10.38124/ijisrt/26jan1615

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Heart Disease, also known as CVD, represents one of the major health issues worldwide. There is an urgent need for the availability of risk prediction systems that are reliable yet non-invasive in nature to facilitate timely clinical interventions. This study aims to explore how well the Extra Trees Classifier performs in predicting the risk of heart disease. The Extra tree-based method represents an advanced ensemble ML approaches that was trained on a comprehensive dataset containing 20 key clinical and lifestyle attributes of patients. In addition, this approach was carefully tuned and thoroughly evaluated to ensure reliable performance. Feature analysis plays the main role in this paper by ranking the most influential predictors of CVD risk according to their importance. This allows drawing data-driven conclusions that can inform clinically oriented risk assessment analyses. Based on the results described above, the Extra Trees Classifier is effective and reliable for predictive cardiology and thus serves as a good starting point for improved clinical decision-making.

Keywords : Cardiac Risk Assessment, Machine Learning, Extra Trees-Based Approach, Ensemble Learning, Clinical Parameters, Predictive Modeling, Feature Importance, Kaggle Dataset.

References :

1. Nicholas, G.Hoendarto, and J.Tjen, "cardiac risk Prediction with Decision Tree," Social Science and Humanities Journal, vol. 9, no. 1, pp. 6451-6457, Jan. 2025, doi: 10.18535/sshj. v9i01.1444.
2. H. Al Amin, S. Wibisono, E. Lestariningsih, and M. L. M.A, "Optimizing cardiac risk Prediction with Random Forest and Ensemble Methods," COGITO Smart Journal, vol. 11, no. 1, pp. 180-[Page Numbers], June 2025.
3. Sakyi-Yeboah et al., "cardiac risk Prediction Using Ensemble Tree Algorithms: A Supervised Learning Perspective,” Applied Computational Intelligence and Soft Computing, vol. 2025, Art. ID 1989813, 18 pages, 2025, doi: 10.1155/acis/1989813.
4. Xia, "Influencing Factors and Prediction of Heart Disease,” Highlights in Science, Engineering and Technology, vol. 123 (BFSPH 2024), pp. 586-592, 2024.
5. Jiang, "cardiac risk Prediction Using Machine Learning Algorithms," Master's Thesis, University of California, Los Angeles, 2020.
6. M. Meti and Dr. Lingraj, "Heart Boost: Clinical Data-Driven cardiac risk Prediction Using XGBoost,” International Research Journal on Advanced Engineering Hub (IRJAEH), vol. 3, no. 9, pp. 3517-3525, Sep. 2025, doi: 10.47392/IRJAEH.2025.0517.
7. A.T L, A. BK, and D. D, "cardiac risk Prediction Using Logistic Regression,” Indian Journal of Computer Science and Technology, vol. 4, no. 2, pp. 356-359, May-Aug. 2025, doi: 10.59256/indjcst 20250402048.
8. F. Y. Ayankoya et al., "cardiac risk prediction using machine learning model,” Global Journal of Engineering and Technology Advances, vol. 24, no. 2, pp. 036-049, 2025, doi: 10.30574/gjeta 2025.24.2.0223.
9. B. Shehzadi et al., "cardiac risk Prediction Statistical Analysis and Classification of cardiac riskUsing Clinical Parameters,” Social Sciences & Humanity Research Review, Jan.-Mar. 2025, pp. [Page Numbers], ISSN: 3007-3162.
10. Y. Chen, "Predicting cardiac risk Using Machine Learning: Analysis and New Insights,” Dean&Francis [Journal Title Implicit], pp. [Page Numbers], ISSN: 2959-6157.
11. S. Chaudhari, C. S. Gautam, and A. A. Waoo, "Optimizing cardiac risk Prediction Accuracy using Machine Learning Models,” International Journal of All Research Education and Scientific Methods (IJARESM), vol. 12, no. 6, June 2024, pp. [Page Numbers], ISSN: 2455-6211.
12. V. V. R. Karna et al., "A Comprehensive Review on cardiac risk Prediction using Machine Learning and Deep Learning Algorithms,” Archives of Computational Methods in Engineering, pp. [Page Numbers], 2024, doi: 10.1007/s11831-024-10194-4.
13. Anjali Regala, SD Ravikanti, and RG Franklin, “Design and implementation of cardiac risk prediction using naive Bayesian", International conference on trends in electronics and informatics (ICOEI), pp. 292-297.
14. VV Ramalingam, A Dasapopath and MK. Raja, "cardiac risk prediction using machine learning techniques-a survey", International journal of Engineering & Technologies, Vol. 7, no. 5.8, pp. 684-7.
15. E. I. Elsedimy, S. M. M. Abo Hashish, and E. Alzgara, "New cardiovascular disease prediction approach using support vector machine and quantum-behaved particle swarm optimization", Multimedia Tools and Applications, 2023.