Published Paper Details:

Diabetes remains a critical global health concern, necessitating advanced solutions for early detection and management. This project focuses on a data mining-driven approach to develop a predictive system for diabetes using the Pima Indians Diabetes Dataset. The system incorporates a suite of data mining techniques and machine learning algorithms, including Logistic Regression, Extreme Gradient Boost (XGBoost), and Decision Tree, to analyze and interpret critical health metrics such as glucose levels, BMI, blood pressure, and insulin concentrations. The project follows a robust data mining process comprising data preprocessing, feature selection, and model evaluation. Data cleaning and normalization ensure quality and consistency, while feature selection optimizes model performance. The system applies advanced algorithms to identify hidden patterns and generate personalized diabetes risk scores, enabling early intervention and preventive care. This data mining approach empowers healthcare professionals with actionable insights through an interactive interface featuring real-time analytics and comprehensive reporting. By leveraging data mining algorithms, this system demonstrates its potential to enhance clinical decision-making, improve early diagnosis, and contribute to better health outcomes.