AyurSage: An AI-Assisted Ayurvedic Clinical Decision Support System Using Canonical Disease-Dosha-Symptom Patterns

Authors:

Jagveer Singh Bedi¹, Aditi Verma², Diksha Sharma³, Yashi Upadhyay⁴, Ayushi Sharma⁵, Diwakar Shrivastava⁶, Anshika Singh⁷

^{1,2,3,4,5,6,7}Department of Computer Science & Engineering / Information Technology, Amity University, Lucknow, India

Abstract - Ayurveda emphasizes personalized diagnosis through the balance of three doshas: Vata, Pitta, and Kapha. While Artificial Intelligence (AI) has shown promise in healthcare, most AI-driven Ayurvedic systems rely on questionnaire-based or limited datasets without grounding in classical knowledge. This paper presents AyurSage, an AI-assisted Clinical Decision Support System (CDSS) that integrates canonical Ayurvedic knowledge with machine learning. A two-level data model is introduced, consisting of structured disease-dosha-symptom-treatment patterns extracted from classical texts and synthetic patient instances generated through controlled variation of contextual features. A five-phase pipeline transforms textual data into a structured dataset with 16 features and ~3000 records. A Random Forest classifier achieves perfect accuracy for dosha prediction, while a tuned ensemble model achieves ~95-97% accuracy for disease prediction. A knowledge-based recommendation system ensures treatment traceability to canonical sources. The system is deployed as a full-stack web application supporting real-time inference. AyurSage demonstrates a scalable, interpretable, and epistemologically grounded approach to AI in traditional medicine.

Key Words: Ayurveda, Clinical Decision Support System, Machine Learning, Explainable AI, Dosha Prediction, Text Mining