Discrete Optimization Analysis of Ensemble Learning in Ayurvedic Dosha Classification

This study introduces a unique framework that combines machine learning, and dosha profiling from Ayurveda, to improve precision, reliability, and interpretability of traditional diagnostic assessments. Four machine learning algorithms (Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBoost) were systematically investigated with a rigorous, quiz-based dataset that contained demographic, lifestyle, and physiological characteristics to classify six dosha categories (Vata, Pitta, Kapha, and their pairs). The experimental results showed a stark difference between linear and ensemble approaches. With an accuracy of 30% (F1 = 0.288), Logistic Regression provided marginal performance, suggesting there is limited separability in the overlapping patterns of health, while SVM came with an accuracy of 97.3% (F1 = 0.972) with kernel optimization. However, tree-based ensemble approaches improved predictive utility; Random Forest showed the highest overall performance (accuracy = 98.1%, F1-Macro = 0.982), with XGBoost and a Stacked Ensemble model behind (both ≈ 98%). This confirms ensemble approaches can represent the complex and nonlinear interdependencies associated with holistic wellness datasets. Interpretability analysis through feature importance ranking identified lifestyle and physiological variables—including sleep quality, appetite, emotional stability, skin texture, and digestion pattern—as the most important predictors, demonstrating a strong correlation to established Ayurvedic theory. Additionally, a desktop-based, interactive visualization was built to allow dosha prediction and wellness insights in real time. In conclusion, this work provides justification for Random Forest and XGBoost models as benchmarks for dosha classification and achieves a scientific syntheses of ancient Ayurvedic practice with contemporary machine learning. The results have important implications for the portfolio of digital Ayurvedic practice; to support data-informed personalized medicine; to foster new cross-disciplinary collaborations among ancient medicine, modern artificial intelligence and machine learning. This study compares Support Vector Machines (SVM), Random Forest (RF), and XGBoost for Ayurvedic dosha classification (Vata, Pitta, Kapha).