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@article{206752,
        author = {Sangam S M and Sahana Bandekar and S Arpitha and Vinayak},
        title = {Diabetes Prediction Website Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {315-318},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206752},
        abstract = {Diabetes is one of the most prevalent lifestyle diseases affecting patients of all ages. Timely detection of diabetes can prevent severe conditions like cardiovascular disease, renal failure, and visual impairment. In this paper, a web-based application is proposed to identify whether a patient suffers from diabetes using machine learning models. The system uses variables like glucose concentration, body mass index (BMI), age, insulin concentration, and blood pressure to evaluate diabetes risk. Multiple algorithms, including Logistic Regression, Decision Tree, and Random Forest, are analyzed and contrasted based on performance. Ultimately, the final model is deployed in an easy-to-use web page that enables patients to diagnose themselves.},
        keywords = {Diabetes, machine learning, prediction system, web application, healthcare analytics.},
        month = {July},
        }