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@article{175205,
        author = {Preetha Venkatachalam and Sushma Ramakrishnan and Swedha P and Mrs.N.G.Dharaniya},
        title = {Clinical Interaction-Based Drug Guidance System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2313-2318},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175205},
        abstract = {Online recommender systems are being used increasingly often for hospitals, medical professionals, and drugs. Today, the great majority of consumers look online before asking their doctors for prescription suggestions for a range of health conditions. The medical suggestion system can be valuable when pandemics, floods, or cyclones hit. In the age of Machine Learning (ML), recommender systems give more accurate, precise, and reliable clinical predictions while using less resources. The medicine recommendation system gives the patient reliable information about the medication, the dosage, and any possible adverse effects. Medication is given based on the patient's symptoms, blood pressure, diabetes, temperature, and other parameters. Drug recommendation systems provide precise information at any time while improving the performance, integrity, and privacy of patient data in the decision-making process. Recommender system, the decision tree produces the most accurate results. In times of medical emergency, a drug recommendation system is helpful for giving patients recommendations for safe medications.},
        keywords = {Machine learning, Decision tree, Recommender systems, Medicines},
        month = {April},
        }