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@article{182842,
        author = {Pranav Prakash and Saurabh Kumar Singh and Chandan Kumar Singh and Dasarath Rana and Dr Amit Shrivastava and Mr Abhishek Malviya},
        title = {Heart Risk Predictor},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3669-3675},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182842},
        abstract = {heart disease is one of the leading causes of death globally. Predicting cardiovascular risk in the early stages can significantly reduce mortality rates and improve patient out- comes. This paper presents a machine learning-based solution for predicting heart disease risk using features such as age, cholesterol, blood pressure, smoking status, and diabetes. The system employs Linear Regression and Multivariable Polynomial Regression models trained on a dataset of 6,644 instances. A web interface built using Flask allows users to input health parameters and receive a risk score. The multivariable polynomial regression model achieved an accuracy of 75.8%. The paper also presents a comparative literature review of seven studies in heart disease prediction and discusses the implementation, sample circuit diagrams, code, and results.},
        keywords = {},
        month = {July},
        }