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@article{183670,
        author = {SAMPATH KUMAR S and santhosh SG and Vindhya HG},
        title = {Integrating AI into Ayurvedic Practice for Precision Dosage Recommendations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2566-2573},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183670},
        abstract = {The Artificial Intelligence with ayurveda is long-standing(ancient) Indian system medicine which offers promising direction for personalized healthcare. By integrating traditional ayurvedic concepts as Prakriti and Vikriti with machine learning algorithms. It aims to enhance the treatment accuracy and patient outcomes. The system predicts the user proclivity by analyzing the ratings. Artificial Intelligence accelerates the herbal medicines discovery and formulates to ensure the drug integrations. The tools will help to generate the dosage plans for adaptability to change the patient health and real-time feedback which offers the lifestyle advice on ayurvedic principles. It evaluates and compares the fulfillment of three classifier algorithms they are Multinomial Naïve Bayes, Random Forest Classifier and XGBoost, Here the main algorithm used is MNB. Two additional algorithms are evaluated in comparison to Random Forest Classifier and XGBoost. The main purpose of this comparison is to find best predictor},
        keywords = {Artificial Intelligence, Ayurveda, Dosage Optimization, Multinomial Naïve Bayes, Personalized Treatment, Random Forest, XGBoost.},
        month = {August},
        }