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@article{196742,
        author = {Muskaan Refai and Diya Ravrane and Kshitija Rakshikar and Atharva Rane and Kapila Moon},
        title = {Heart Disease Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8223-8229},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196742},
        abstract = {The main reason for worldwide fatalities remains heart disease because it requires both fast detection and precise risk assessment to prevent additional deaths. The AI system CardioPredict operates as a smart computer program which evaluates heart disease risk through analysis of vital health infor- mation including age and cholesterol levels and blood pressure readings and chest pain types. The AI system enables doctors to forecast heart conditions through thorough evaluation of essential medical indicators. The project integrates data science with medical technology to develop a Python and Flask-based website which enables real-time heart problem detection. The researchers tested multiple computer learning methods including Logistic Regression and Decision Tree and Random Forest to achieve optimal results. Our research demonstrated that uniting these algorithms produced a 94.6% accuracy rate for heart disease risk prediction. The AI system CardioPredict demonstrates how artificial intelligence enables doctors and patients to make fast choices about early detection and preventive medical care.The project establishes a connection between machine learning and medical applications because it fulfills requirements for large- scale predictive healthcare analytics research.},
        keywords = {},
        month = {April},
        }