Predictive Modeling for Diabetes Care: Using Machine Learning to Anticipate Glucose Variations and Potential Health Risks
Keywords:
Diabetes prediction, Machine learning models, Glucose variability forecasting, LSTM networks, Random Forest classifier, XGBoost, Continuous glucose monitoring, Hypo-hyperglycemia prediction, Risk stratification, Digital health analytics.Abstract
Diabetes mellitus remains a major global health challenge, driven by rising incidence rates and the serious complications it precipitates, including neuropathy, nephropathy, and cardiovascular dysfunction. With the growing need for early detection and targeted intervention, machine learning has emerged as a powerful approach for anticipating glucose instability and flagging early markers of metabolic decline. In this study, predictive models built using Random Forest, XGBoost, and Long Short-Term Memory networks are designed to estimate both immediate and long horizon glucose variations among diabetic patients. These models draw on a rich blend of continuous glucose monitoring streams, electronic health records, and lifestyle-based metrics to create a comprehensive training environment. Their performance is examined through the prediction of hypo and hyperglycemic episodes, supported by interpretability tools and feature relevance assessments to ensure clinical transparency. Across all experiments, temporal deep learning architectures especially LSTM stand out by offering higher predictive accuracy, greater robustness, and stronger adaptability to individualized physiological rhythms than traditional machine learning methods. Overall, the study underscores the considerable promise of machine learning driven prediction systems in advancing personalized diabetes care, refining risk assessment processes, and enhancing digital health infrastructures for proactive, data informed disease management.
