Enhancing Healthcare with Personalized Recommendations: A Machine Learning Approach to Symptom-Driven Diagnosis

This paper introduces the Personalized Medical Recommendation System, a machine learning-based platform that supports users in symptom analysis and personalized healthcare management. Users input symptoms through a simple interface, and machine learning models predict potential diseases. The system offers tailored recommendations, including suggested medications, prescription details, and exercise routines. Built on a Flask web application, it provides accessible healthcare support with a strong focus on data privacy and security. Continuous improvement through user data refines predictive accuracy, advancing personalized medicine. This study outlines the system's design, implementation, and performance, underscoring its potential to enhance health outcomes. Key Words: personalized healthcare, machine learning, disease prediction, medical recommendation system, symptom analysis, data privacy.