RX Assist - Smart Disease Prediction and Drug Recommendation

The growing volume of healthcare data necessitates advanced data mining techniques to extract meaningful patterns and insights. This paper introduces “RX Assist,” an Intelligent Disease Prediction and Drug Recommendation system employing multiple machine learning algorithms. Leveraging patient symptoms, age, and gender as input parameters, the system predicts diseases using Gaussian Naive Bayes, Random Forest, Logistic Regression, and Sklearn Decision Tree models, achieving high accuracies (98.4% to 100%). The drug recommendation module utilizes Random Forest and Gaussian Naive Bayes models, achieving notable accuracies (100%). The project also incorporates user-friendly interfaces for patients and doctors, enabling appointment bookings and fostering efficient communication. Our work extends the base paper by enhancing disease prediction accuracy and incorporating a sophisticated drug recommendation system. Experimental results and methodologies presented herein contribute valuable insights for future medical applications and research endeavors.