MACHINE LEARNING BASED DISEASE PREDICTION & DRUG RECOMMENCEMENT SYSTEM

Dr. Shiksha Dubey, Dr. Bhakti Pimpale

Humanity has long battled infectious diseases that continue to evolve and pose significant public-health challenges. This study presents a machine learning–based Disease Prediction and Drug Recommendation System that analyzes symptom patterns to support preliminary disease identification and assist healthcare professionals in treatment selection. The system is intended as a clinical decision-support tool rather than a replacement for medical diagnosis. Using symptom and prescription datasets, Decision Tree–based models were developed for disease classification and drug recommendation. Experimental evaluation demonstrated high predictive performance, with accuracy, precision, recall, and F1-score used as the primary assessment metrics. The proposed framework highlights the potential of machine learning in supporting healthcare decision-making while emphasizing the need for clinical validation before real-world deployment.