The Medicine Recommendation System is designed to suggest accurate medications based on patient symptoms, leveraging advanced machine learning techniques. Traditional systems like decision trees and Gaussian Naive Bayes are commonly used but are limited by their inability to handle complex symptom-drug relationships effectively. These systems often require significant manual data preprocessing and produce less personalized recommendations. To overcome these challenges, this project proposes the use of a Convolutional Neural Network (CNN) to enhance the accuracy and personalization of medication recommendations. CNNs are capable of analyzing large datasets and automatically identifying intricate patterns between symptoms and drugs. By doing so, the system delivers more context-aware, personalized treatment suggestions while reducing the risk of adverse drug reactions. The Medicine Recommendation System is especially valuable in emergency situations or remote areas where immediate access to healthcare professionals may not be available. The system improves decision-making efficiency by minimizing manual intervention, reducing operational costs, and offering timely, accurate recommendations. Ultimately, the project aims to improve healthcare outcomes by providing personalized, data-driven solutions to medication recommendations.

Keywords: Recommendation, Medicine, symptoms.