Copyright © 2025 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{178063,
        author = {Shashwat Srivastava and Sayyed Zamin Abbas and Harsh Kesharwani},
        title = {RESEARCH ON MACHINE LEARNING ALGORITHM ON HEART DISEASE PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3302-3308},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178063},
        abstract = {Preventative medical intervention depends on early heart attack identification because heart attacks represent the globe's most detrimental group of mortality causes. Heart attack predictions now become possible thanks to machine learning (ML) improvements that develop precise prediction models. The research explores and breaks down three key investigations regarding the use of machine learning for heart attack prediction. The study uses Decision Tree (DT) Random Forest (RF) and Support Vector Machine (SVM) and Gradient Boosting to find the most suitable machine learning algorithm. The research indicates that Support Vector Machines (SVM) functions effectively for healthcare prediction while demonstrating 91.67% accuracy as its top performance. Heart disease detection and support vector machines and random forest together with machine learning represent the main discussion points in this analysis.},
        keywords = {},
        month = {May},
        }