PREDICTIVE AND RECOMMENDATION BASED HOLISTIC HEALTHCARE SYSTEM USING MACHINE LEARNING

Abstract

The convergence of traditional and modern healthcare systems represents a transformative step toward achieving holistic and personalized medical care. In recent years, the rapid advancement of artificial intelligence (AI) and ma- chine learning (ML) techniques has enabled the develop- ment of intelligent systems capable of predicting diseases and recommending suitable treatments. This study intro- duces a comprehensive disease prediction and healthcare remedy recommendation system that integrates multiple do- mains of medical knowledge, including Ayurveda, Siddha, traditional home remedies, and modern allopathic medicine, within a unified and user-friendly digital platform. The primary objective of this system is to provide users with

accurate disease predictions based on their symptoms and to recommend scientifically validated as well as tradition- ally recognized remedies, thereby bridging the gap between conventional and contemporary healthcare approaches.The proposed framework employs several machine learning algo- rithms—namely Decision Tree (DT),Random Forest (RF), Gradient Boosting Classifier (GBC), and Support Vector Machine (SVM)—to identify the most effective model for disease prediction. Analysis of these algorithms is performed to determine the most accurate and efficient predictive model. The selected algorithm is then integrated into the healthcare recommendation engine, which aligns the predicted disease with relevant treatments from various healthcare systems. This multi-domain recommendation strategy ensures that users receive holistic healthcare advice encompassing pre- ventive, curative, and lifestyle-based remedies.