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@article{176041,
        author = {Kamapu Srinivasrao and priyavaijayanthi and jayaprada and mrudula and sai sailesh and sumanth},
        title = {medical recommendation system using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4967-4970},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176041},
        abstract = {Advancements in healthcare have facilitated the use of artificial intelligence (AI) for medical decision-making. A key focus area is medical prescriptions, where machine learning (ML) can assist in suggesting appropriate treatments based on patient symptoms and their medical background. This paper introduces a real-time Medical Recommendation System that employs machine learning algorithms to forecast medications for conditions such as kidney disease, liver disease, pneumonia, malaria, lung cancer, diabetes, and heart disease. The machine learning models were developed using disease-specific datasets, achieving a high level of accuracy in determining the right disease. The system features an easy-to-use web interface, enabling healthcare providers and patients to receive tailored medication recommendations on the spot. Our findings suggest that machine learning can greatly improve the precision and effectiveness of medical recommendations, reducing human errors in prescribing practices.},
        keywords = {Recommendation system, Machine learning, Medical, Healthcare},
        month = {April},
        }