International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Exploring Ayurvedic Medicine Recommenda-tion using Machine Learning Techniques

Author(s) Ligandro Singh Yumnam, Aditya Jain, Usha G, C. Pretty Diana Cyril
Country India
Abstract Ayurveda, a time-tested medical system, traditionally offers personalized healthcare. Recently there has been a growth in medicine recommendation using AI but not much has been explored about Ayurvedic medicines. With our paper we plan to implement and explore Ayurvedic medicine recommendation
and how machine learning can enhance this approach by recommending individualized Ayurvedic treatments based on patient data. We propose a system that uses machine learning methods such as decision trees and neural network to first diagnose and then recommend the natural medicines. Furthermore, our main objective is to explore the potential of machine learning principles in medicine recommendation. By integrating machine learning techniques, this research seeks bridge the gap between traditional Ayurvedic wisdom and modern machine learning. The primary methodology employed in this study involves the training of a Neural Network model using patient data and predict medicines. The proposed system has the potential to improve healthcare accessibility and efficacy, particularly within the context of personalized Ayurvedic recommendations.
Field Computer Applications
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-01
Cite This Exploring Ayurvedic Medicine Recommenda-tion using Machine Learning Techniques - Ligandro Singh Yumnam, Aditya Jain, Usha G, C. Pretty Diana Cyril - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.15921
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.15921
Short DOI https://doi.org/gtpw9g
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