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@article{177045,
        author = {Mangesh I. Bonde and Santosh S. Mhaske and Rushikesh V. Takwale and Virendra D. Gawande and Om V. Warade and Komal K. Nahate},
        title = {Heart Disease Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {309-315},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177045},
        abstract = {Cardiovascular diseases are a leading cause of death worldwide, with 17.9 million deaths annually. This research explores machine learning algorithms for predicting cardiac abnormalities using ECG analysis and symptom-based data. Four classification algorithms (Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbors) were compared using a dataset of 1025 patient records from Kaggle and PhysioNet. The study highlights AI's potential in enhancing diagnosis, particularly in emergency situations. Random Forest achieved high classification accuracy. AI-driven diagnostic tools can improve early detection, especially in resource-limited settings. Future research will focus on refining models and ensuring clinical utility.
The successful integration of machine learning models into clinical practice can revolutionize cardiovascular care, making early detection and intervention more accessible, especially in underserved areas. Future research will focus on refining the models, improving their interpretability and ensuring that they are clinically useful, ensuring that AI models become an indispensable tool in the diagnosis and management of cardiovascular diseases.},
        keywords = {Machine Learning, AI, Random Forest, KNN, Decision Tree, Logistic regression, VS Code, Confusion matrix, Heatmap, AUC, Accuracy .},
        month = {April},
        }