Hybrid Machine Learning for Personalized Ayurvedic Drug Recommendation via Prakriti Assessment

Ayurveda, an ancient system of medicine, emphasizes personalized treatment based on individual body constitutions, known as Prakriti. In this paper, we present a hybrid machine learning approach for recommending Ayurvedic drugs, integrating traditional Prakriti assessment with modern symptom-based analysis. Using clustering and classification algorithms, the system analyzes patient symptoms and body constitution to generate personalized drug recommendations. This method avoids the computational complexity of deep learning, providing an interpretable, efficient, and scalable solution. Experimental results demonstrate the system's accuracy in identifying Dosha imbalances and its effectiveness in recommending appropriate treatments.