A Machine Learning-Based Recommendation System for Disease Prediction

A key strategy for enhancing clinical judgment and tailored medicine is the incorporation of machine learning into healthcare systems. This study presents a Medicine Recommendation System (MRS) that uses a variety of machine learning models to recommend drugs based on disease prediction. Among these models are K-Means Clustering, Random Forest, Support Vector Classifier (SVC), Naive Bayes, Logistic Regression, and Gradient Boosting. In order to anticipate the likely condition and provide a suitable drug, the system makes use of patient data, including symptoms, demographics, and medical history. Accuracy, precision, recall, F1 score, and confusion matrix measures are used to assess each model's performance.