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@article{178411,
        author = {Mamatha S and LIKHITH H S and LIKHITH KUMAR N and Mohammed Jawad and Mohammed aqib},
        title = {Django Based Health Risk Evaluation Application With Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3639-3643},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178411},
        abstract = {This project presents a Django-based web application for health risk evaluation using machine learning. The application collects user health data, including demographics, medical history, and lifestyle factors, to assess risks for conditions like neurological disorders, obesity, and diabetes. Leveraging models such as Random Forest and K- Nearest Neighbors (KNN), it provides real-time health predictions with high accuracy. The system categorizes users into risk levels—low, moderate, or high—offering personalized recommendations. Data visualization tools enable users to track their health status over time. A secure and user- friendly interface ensures accessibility for both individuals and healthcare professionals. The application is designed to continuously improve through iterative training on diverse datasets. By integrating AI-driven insights with modern web technologies, it enhances preventive healthcare accessibility. The project demonstrates the potential of machine learning in early disease detection and proactive health management. Future enhancements include integration with wearable devices and AI-based consultations.},
        keywords = {},
        month = {May},
        }