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Ayurvedic Medicine Recommendation
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Ayurveda, an ancient Indian medical system, offers personalized healthcare through the assessment of Prakriti (constitution), dosha imbalance, symptoms, and lifestyle factors. However, Ayurvedic diagnosis and medicine selection often depend heavily on expert practitioner interpretation, making it difficult to scale and standardize. With advances in artificial intelligence (AI) and machine learning (ML), personalized recommendation systems can improve the accessibility, consistency, and efficiency of Ayurvedic healthcare. This paper presents a machine learning?based framework for Ayurvedic medicine recommendation using structured patient data, dosha assessment, and symptom analysis. A standardized ontology for Ayurvedic concepts (diseases, herbs, formulations, and dosha associations) was developed to address heterogeneity in classical terminology. The proposed system uses supervised learning models?Decision Trees, Random Forests, and Support Vector Machines (SVM)?to predict appropriate Ayurvedic formulations. An NLP-based component extracts therapeutic associations from Ayurvedic texts. Experiments on a curated dataset of 1,500 patient records show that Random Forest achieves the highest accuracy (94.8%) for medicine recommendation. The study highlights the potential of ML to augment Ayurvedic clinical decision support, while also discussing limitations and ethical issues.
References
[1] Patel, R., et al., “Herbal Medicine Recommendation Using Machine Learning,” International Journal of Computer Applications, 2021.
[2] Sharma, P., & Verma, S., “Decision Tree Based Ayurvedic Recommendation System,” IJCSIT, 2022.
[3] Kumar, A., et al., “Predictive Analysis of Ayurveda Medicine using ML,” Journal of AI in Healthcare, 2020.
[4] “Ayurvedic Principles and Treatments,” National Institute of Ayurveda, India.
[5] Metha, D., &Reddy, S. (2023), “Integrating Ayurvedic Knowledge into AI Systems, “Journal of Intergative Medicine Research”.
[6] Singh, R., et al (2024),” Enhancing traditional Medicine Prediction Models using Deep Learning,” IEEE AccessDash, B., & Sharma, R. K. (2014). Caraka Samhita: Text with English Translation. Chowkhamba Sanskrit Series Office.
[7] Vagbhata. (2017). Ashtanga Hridayam (K. R. Srikantha Murthy, Trans.). Chaukhamba Krishnadas Academy.
[8] Sushruta. (2016). Sushruta Samhita: English Translation of Text and Dalhana’s Commentary (A. A. Shastri, Trans.). Chaukhamba Sanskrit Pratishthan.
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How to cite this paper
@article{1712536,
author = {Famiya Shariff, Lamiya Huda A, Mohammed Maaz HK, Mohammed Zeeshan, Abdul Rehman},
title = {Ayurvedic Medicine Recommendation},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {68-75},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712536.pdf},
abstract = {Ayurveda, an ancient Indian medical system, offers personalized healthcare through the assessment of Prakriti (constitution), dosha imbalance, symptoms, and lifestyle factors. However, Ayurvedic diagnosis and medicine selection often depend heavily on expert practitioner interpretation, making it difficult to scale and standardize. With advances in artificial intelligence (AI) and machine learning (ML), personalized recommendation systems can improve the accessibility, consistency, and efficiency of Ayurvedic healthcare. This paper presents a machine learning?based framework for Ayurvedic medicine recommendation using structured patient data, dosha assessment, and symptom analysis. A standardized ontology for Ayurvedic concepts (diseases, herbs, formulations, and dosha associations) was developed to address heterogeneity in classical terminology. The proposed system uses supervised learning models?Decision Trees, Random Forests, and Support Vector Machines (SVM)?to predict appropriate Ayurvedic formulations. An NLP-based component extracts therapeutic associations from Ayurvedic texts. Experiments on a curated dataset of 1,500 patient records show that Random Forest achieves the highest accuracy (94.8%) for medicine recommendation. The study highlights the potential of ML to augment Ayurvedic clinical decision support, while also discussing limitations and ethical issues.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712536}
}