Disease Prediction and Medication Recommendation Using Machine Learning

Abstract—Advancements in healthcare have greatly benefited from machine learning, especially in disease prediction and medication recommendations. This project introduces a Python-based machine learning model designed to predict potential diseases based on user-reported symptoms and suggest suitable medications. We implemented and evaluated four machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Naïve Bayes—to determine the most accurate model for deployment, ensuring reliable predictions. To make the system user-friendly, we developed an interactive graphical interface using Tkinter. This allows users to easily input their symptoms and receive potential diagnoses, along with relevant precautions and medication suggestions. The recommendation system identifies appropriate pharmaceutical salts for the diagnosed condition, improving medication accuracy and guidance. By comparing multiple machine learning models, our approach ensures that the most accurate algorithm is selected, enhancing the reliability of disease predictions. The intuitive GUI makes healthcare support more accessible, even for individuals without medical expertise, bridging the gap between users and early healthcare assessments. Future improvements will focus on expanding the dataset, integrating deep learning techniques for better accuracy, and connecting with real-time medical databases to provide up-to-date medication recommendations. This project highlights the potential of artificial intelligence in transforming early disease detection and medication guidance, ultimately improving healthcare accessibility and decision-making. Keywords—Machine Learning, Disease Prediction, Medication Recommendation, Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Naïve Bayes, Graphical User Interface (GUI), Healthcare Accessibility, Artificial Intelligence.