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

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

Khan Sohail *1, Khan Hadi*2, Khan Abdullah*3, Sanica Chavan*4, Dr. Anil Kale*5

*1,2,3,4 Student at Mahatma Gandhi Mission College of Engineering and Technology, Mumbai, Maharashtra, India.

*5Associate professor at Mahatma Gandhi Mission College of Engineering and Technology, Mumbai, Maharashtra, India.

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Abstract - 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.

Key Words: Drug Recommendation, Machine Learning, NLP, Patient Feedback, Healthcare.