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@article{181812,
        author = {Vishakha Bhagwan Damodhar and Madhura Sanjay Gangurde and Rutuja Shrimant Wankhede and Sayali Dnyaneshwar Mote and Sonali Kiran Shewale},
        title = {Medicine Recommendation System using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5516-5521},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181812},
        abstract = {The core objective of the project is to develop a machine learning-based disease prediction and medicine recommendation website capable of harnessing individual health data to generate personalized health recommendations, fostering early detection and more effective management of health issues. Utilizing machine learning (ML), the team aims to predict diseases like joint pain, burning micturition, abdominal pain, and irregular sugar levels for early intervention and adapting diagnosis strategies. In their approach, ML algorithms, including the gradient boosting algorithm (GBA), analyze diverse health data sources to build a comprehensive recommendation system. All models demonstrate an accuracy rate of over 100%, highlighting the system's reliability and effectiveness. By integrating various health data sources and focusing on proactive health management, this initiative has the potential to transform health practices. It empowers individuals to make informed decisions regarding their well-being and fosters improved health outcomes.},
        keywords = {The associated with this initiative include individual data, personalized health recommendations, early detection, machine learning, diverse health data sources, a recommendation system, informed decisions, and improved health outcomes},
        month = {June},
        }