Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{173877,
        author = {P.Meghana and P.P.N.G. Phani Kumar and G.Tejasri and B.Dilliswari and Mohammad Abdul Basit},
        title = {Harnessing Ensemble models for Cardiovascular Stroke Forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2606-2611},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173877},
        abstract = {cardiovascular diseases execute the highest number of deaths internationally and disregard economic levels or geographical boundaries. The identification of cardiovascular conditions at an early stage alongside quick medical care substantially decreases disease prevalence while improving medical results. A complete Cardiovascular Disease Prediction System will be developed through machine learning algorithms and ensemble techniques according to this project's objective. The prediction system uses Logistic Regression along with K- Nearest Neighbors, Random Forest, Decision Tree and XG-Boost and Ensemble Learning to assess multiple cardiac health signals which help determine cardiovascular event risks. This predictive system incorporates multiple models to reach high reliability and accuracy thus enabling healthcare professionals to take proactive healthcare measures. The innovative solution provides medical staff with productive information to boost diagnostic accuracy alongside preventive healthcare initiatives.},
        keywords = {Ensemble Learning, Machine Learning, Voting Classifier, Logistic Regression, K-Nearest Neighbors, Random Forest, Decision Tree, XG-Boost.},
        month = {March},
        }