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@article{174010,
        author = {Prof. Ghadge S. V. and Prof. Shah Saloni Niranjan and Samir Bhosale and Onkar Gadade and Ganesh Kalaskar},
        title = {A Survey on Exploring Ayurvedic Medicine Recommendation Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2151-2154},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174010},
        abstract = {The integration between machine learning (ML) and Ayurveda, India's ancient holistic system of medicine, has been effectively modeled in this paper to develop a system for personalized healthcare recommendations. Ayurvedic prescriptions are based on individualized treatment modalities according to one's dosha (body constitution), lifestyle, and symptoms. This system intends to align traditional knowledge from Ayurveda with cutting-edge computational techniques. Decision trees are utilized for disease prediction, whereas neural networks are employed to recommend treatment. The proposed method analyzes individual patient data, including symptoms and medical history, and recommends appropriate and easy Ayurvedic treatment. Such integration increases the accuracy of diagnosis, automates treatment recommendations, and allows the practitioner of Ayurvedic medicine to scale up their services to reach a larger audience. The study illustrates how ML may assist Ayurveda in achieving optimization, i.e., working better for Ayurveda so that it may work better for modern health-care scenarios. In short, the study discusses the promise of AI-led Ayurvedic treatments contributing toward the personalized and preventive healthcare delivery model.},
        keywords = {Ayurveda, Machine Learning, Decision Trees, Random Forest, Personalized Medicine.},
        month = {March},
        }