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{163844,
        author = {Vishwajeet Vinod Tayde and Komal Bhimrao Jadhal and Rushali Govardhan Lanjulkar and Radha Vitthal Bakal and Santosh Shriram Mhaske},
        title = {Heart Disease Detection and Classification Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2513-2518},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163844},
        abstract = {Cardiovascular diseases (CVDs) pose a substantial health burden in India, especially in regions with inadequate medical resources. This study aims to identify predictive parameters for CVD detection, specifically focusing on factors accessible in low-resource settings. We present a GUI-based heart disease prediction model utilizing the Random Forest algorithm, offering a user-friendly solution for early detection of CVDs in underserved communities. Through machine learning techniques, our model facilitates efficient risk assessment, contributing to improved healthcare outcomes in areas with limited access to medical facilities.},
        keywords = {Cardiovascular disease, Machine learning, Random Forest, GUI-based model, Prediction, Underserved communities},
        month = {},
        }