Bridging Ayurveda and AI: Data Standardization for Improved Machine Learning Application - Sudha.P, Sudha.P; .K. A, Akash; B. K, Sanjana.; .G. K, Thejaswini (Journal of Neonatal Surgery, 2025)
Ayurveda integration with machine learning (ML) applications must be grounded on a standardized and organized dataset to handle the complexity and heterogeneity of traditional medical terminology. The present paper suggests a process of data standardization in response to the vagueness in Ayurvedic texts to ensure uniformity in disease, symptom, treatment, and dosha categorization. A pre-defined ontology translated raw Ayurvedic terms into standardized terms to improve data quality for ML training. To analyze the impact of standardization, different ML models—Naïve Bayes, CNN, and BERT—were trained on standardized data. The results show that the maximum classification accuracy (100%) was achieved by BERT, which demonstrates the effectiveness of contextual embeddings for Ayurvedic text classification. The findings demonstrate that standardization significantly improves the performance of models, improving knowledge retrieval and compatibility with modern healthcare systems. This research contributes to building robust, machine-usable Ayurvedic datasets for AI-based diagnosis and treatment recommendation in traditional medicine.