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@article{178754,
        author = {R. Sharvesh and Mr. M. Asan Nainar},
        title = {Medincine Recommendation System using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5584-5589},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178754},
        abstract = {The front end of this system is developed using HTML, CSS, and JavaScript, providing an intuitive and responsive user experience. The back end is implemented with Python using the Flask framework, handling the logic, machine learning model integration, and communication with datasets. The project uses Random Forest algorithm to train on a structured dataset of symptoms, severity scores, and disease relationships, ensuring high accuracy in predictions. Additionally, the system accesses datasets containing medicine names, precautions, and dietary recommendations to provide complete healthcare support. This solution can serve as a foundational step toward automated and AI-driven primary healthcare systems, especially useful in rural or under-resourced areas. In the future, this system could be extended to include natural language processing for free-text symptom input, integration with wearable health trackers, or multilingual support to reach a broader audience.},
        keywords = {Machine Learning, Flask, Personalized Medicine, Random Forest, Symptom Prediction, Health Recommendation System, Python, Disease Diagnosis, Medical AI, Healthcare Automation},
        month = {May},
        }