📄 Abstract
Early disease detection plays an important role in improving healthcare outcomes and reducing health risks. This paper presents a Machine Learning Based Disease Prediction System with Health Risk Assessment that helps users obtain preliminary health insights based on symptoms and key health parameters.The system consists of two modules: disease prediction and health risk assessment. The disease prediction module identifies the most probable disease from user-provided symptoms, while the health risk assessment module evaluates factors such as age, blood pressure, blood sugar, BMI, cholesterol level, and heart rate to determine potential health risks.To develop the system, healthcare datasets were preprocessed and analyzed using machine learning algorithms including Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine (SVM). Among these, Random Forest achieved the highest accuracy of 94%. The application was implemented using Python, Flask, Pandas, and Scikit-Learn with a user-friendly web interface.The proposed system provides quick and accessible health insights and can serve as a useful support tool for early disease prediction and health monitoring. While it is not a substitute for professional medical advice, it can assist users in making informed healthcare decisions.
🏷️ Keywords
📚 How to Cite:
Sunil.B , M.Nagaraju , MACHINE LEARNING BASED DISEASE PREDICTION SYSTEM USING PYTHON , Volume 12 , Issue 7, July 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 598 - 603 ,