A Machine Learning Approach to Nadi Pariksha: Detecting Dosha Imbalances

Le résumé fourni par la source

This research introduces PulseVision: AI-based Nadi Pariksha Health Diagnosis, an AI-based system for automated and upgraded traditional Ayurvedic diagnostic process of Nadi Pariksha. The analysis uses a panel-based dataset containing pulse-derived diagnostic attributes that have been preprocessed by the Random Forest classifier to reliably identify the major dosha, i.e., Vata, Pitta, Kapha, or mixture. The 5-fold cross-validation method guarantees the robustness of the model and leads to both high accuracy and reliability of the classification.The model’s performance is evaluated using parameters such as accuracy, precision, recall and f1 score. The accuracy of the random forest was found to be 98.67% with 98.71% precision, 98.67% recall and 98.63 f1 score. The framework is also equipped with interpretability mechanisms, and these are implemented through data visualization methods, specifically, feature distribution histograms, correlation heatmaps, and class distribution plots.The paper illustrates the possibility of combining AI with mainstream medical expertise, hence suggesting a scalable and objective diagnostic tool. Results indicate that PulseVision may be used by Ayurvedic physicians and persons to support data-driven healthcare decisions in Ayurvedic integrative medicine with the potential impact of computational intelligence in the treatment of personalized Ayurvedic medicine.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.