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@article{195842,
        author = {Surabhi Soumika and Anagaya Sameeksha and Tanzeela Rifath and Dr.B.V.Ramana Murthy},
        title = {A Machine Learning based Medicine Recommendation system using random forest and naive bayes using public healthcare datasets},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1148-1156},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195842},
        abstract = {With the increasing availability of healthcare data, machine learning applications are playing a vital role in improving medical decision-making. This study presents a system that utilizes Naïve Bayes and Random Forest classifiers to recommend appropriate medications based on patient symptoms and health conditions, enabling accurate disease prediction. The system is built to manage various user situations. For patients already aware of their diagnosis, it directly offers appropriate medication suggestions, minimizing the need for additional assessment. It also proposes various medicine alternatives, providing flexibility when primary medications are inaccessible or when users desire different options. The system incorporates herbal remedies based on Traditional Chinese Medicine (TCM), providing natural and supplementary health care options alongside conventional medications. By integrating disease prediction, medication recommendations, and herbal guidance, the system offers a comprehensive solution that helps users make informed and effective healthcare decisions.},
        keywords = {},
        month = {April},
        }