End-to-End Machine Learning-Based System for Diabetes Monitoring and Prediction Using Mobile Terminals

Diabetes mellitus is a major chronic disease that requires continuous monitoring and timely risk assessment to reduce the likelihood of severe long−term complications. Recent advances in mobile health technologies and machine learning (ML) provide new opportunities for developing intelligent systems that support diabetes self−management and early risk screening. This paper presents an end−to−end mobile health platform that integrates diabetes monitoring functionalities with an ML−based prediction service within a modular client−server architecture. The proposed system enables users to record glucose measurements, insulin injections, physical activity, and other health−related information while providing historical data visualization, automated reminder notifications, and real−time diabetes risk prediction. The prediction module was trained using the publicly available PIMA Indians Diabetes Dataset and evaluated using Decision Tree (DT), Random Forest (RF), and XGBoost classifiers. Model performance was assessed using accuracy, precision, recall, specificity, F1−score, calibration analysis, and the area under the receiver operating characteristic curve (AUC). Experimental results showed that the RF classifier achieved the highest AUC (0.925), demonstrating superior discrimination capability among the evaluated models and making it the most suitable candidate for deployment within the proposed platform. Although the current prediction model was trained on a benchmark public dataset, the proposed framework provides a practical foundation for integrating ML-driven decision support into mobile health applications and can be further extended through external clinical validation and personalized prediction models.