Predictive Analytics in Diabetes Care: Machine Learning Models for Forecasting Blood Glucose Variability and Complications

Abstract

Diabetes mellitus presents a persistent global health challenge due to its chronic nature and complications arising from fluctuating blood glucose levels. Recent advances in predictive analytics and machine learning have transformed diabetes care by enabling proactive management through accurate forecasting of glycemic variability and complication risks. This study explores a comprehensive predictive framework employing supervised learning algorithms such as Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks for blood glucose forecasting. Using continuous glucose monitoring (CGM) data and patient electronic health records (EHR), models were trained to predict short-term glucose fluctuations and long-term complication probabilities, including neuropathy and retinopathy. Feature selection included clinical, lifestyle, and biochemical parameters to enhance interpretability and accuracy. Evaluation metrics root mean square error (RMSE), mean absolute percentage error (MAPE), and area under the ROC curve (AUC) demonstrated that LSTM achieved superior temporal prediction performance, while Random Forest provided high interpretability for complication risk classification. The results underline that integrating predictive analytics with personalized medicine supports timely interventions and improved glycemic control, significantly reducing hospitalizations and healthcare costs. The study establishes a data-driven foundation for precision diabetes management through machine learning–based predictive intelligence.