Human Prakruti Prediction Using Machine Learning Techniques
Bharati Ainapure, Reshma Nitin Pise, Sonali Bhutad, Ankita Ainapure, Vivek Murlidhar Chaudhari, Siddhesh Suryawanshi · 2025
This paper presents the results of various machine learning methods, including Multimodal Naïve base, Support Vector Machine, Decision Tree, K-Nearest Neighbor,, and Random Forest in predicting human body constituencies. Ayurveda-dosha investigations have long been used; however the quantitative dependability of these diagnostic approaches remains unexplored. Careful and thorough analysis can lead to more effective treatments for predicting body constituencies. The results show that the random forest algorithm, with validation, achieves superior accurateness, recall, precision and F-score (95.2 %). The experimental findings demonstrate that the enhanced model, i.e. random forest classifier based on ensemble learning methods, significantly outperforms traditional approaches. These results suggest that advances in the proposed algorithms could lead to a promising future for Ayurveda treatments using machine learning.