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@article{173982,
        author = {Rashwanth E M and Sasikala and Subramaniam E and Mithun K},
        title = {Heart disease prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2965-2968},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173982},
        abstract = {An inventive application that makes use of machine learning to identify and forecast cardiac illness is the cardiac illness Prediction project. It analyses health variables from a UCI dataset using a Random Forest Classifier to forecast the risk of heart disease. While the frontend is created in Flutter to provide an intuitive user interface for input and outcomes, the backend is constructed with Flask to manage the machine learning model and API endpoints. Predictions are based on important health markers such as age, sex, kind of chest pain, blood pressure, cholesterol, blood sugar, ECG readings, heart rate, and exercise- induced angina. The model guarantees reliable and accurate findings because it was trained on pre-processed data. The project combines Flask with CORS support to provide smooth backend-to-frontend communication while operating locally.},
        keywords = {Heart disease prediction app, Machine Learning, Random Forest, Flutter, Flask},
        month = {March},
        }