Smart medicine recommendation

Abstract

In recent years, the integration of intelligent technologies and machine learning into the healthcare sector has led to the development of digital solutions that enhance patient care and improve diagnostic efficiency. This project introduces an intelligent web-based health assistant that utilizes machine learning techniques to analyze user-reported symptoms and predict potential diseases. Beyond basic diagnosis, the system provides personalized treatment recommendations, including appropriate medications, dietary suggestions, physical activities, and precautionary measures. The implementation incorporates Support Vector Machine (SVM) models trained on structured datasets of symptoms and diseases, ensuring real-time predictions with confidence-based outputs. The platform is designed with role-based access for users, doctors, and administrators, and includes an interactive dashboard for monitoring activities and system performance. The primary objectives of the system are to reduce the diagnostic workload on healthcare facilities, promote proactive health management, and improve accessibility to reliable medical guidance.