Enhancing Medical Prescriptions with Machine Learning: A Symptoms Based Drug Recommendation System

Prescribing the right medication usually depends on a doctor’s experience and general treatment guidelines, but it often misses an important piece of the puzzle — the specific symptoms a patient is experiencing. With more health data now available in digital form, especially information directly shared by patients, there’s a real chance to improve how prescriptions are made using machine learning. This project introduces a Symptom-Based Drug Recommendation System (SB-DRS) that uses Natural Language Processing (NLP) and supervised learning to better understand symptoms, identify likely diseases, and suggest suitable medications. By using techniques like TF-IDF vectorization, Support Vector Machines (SVM), and ensemble models, the system aims to deliver more accurate and personalized drug recommendations.