Decoding Diabetes: Harnessing AI to Accurately Predict Real-Time and Future Blood Glucose Levels for Diabetes Management Using Diet, Exercise, Insulin Intake, and Heart Rate Variability

Overview

Continuous glucose monitoring (CGM) systems are crucial in diabetes care but focus solely on monitoring blood glucose levels (BGL), neglecting diet, exercise, and medication and lacking predictive capabilities. This study introduces Dual Temporal Recurrent Ensemble (DTRE), a novel AI model enabling real-time BGL monitoring without traditional CGM and forecasting BGL for 30- 120 minutes when integrated with CGM data. This research hypothesized that DTRE would achieve superior prediction accuracy in forecasting over multiple time horizons compared to existing models, provide clinically accep table real-time glucose predictions using non -invasive biomarkers, and demonstrate better accuracy with higher-frequency datasets. The model combines advanced machine learning architectures through two parallel branches, integrating key biomarkers including diet, exercise, insulin intake, heart rate, and heart rate variability. This research is the first comprehensive study on developing and testing AI models on two diverse datasets (OHIOT1DM and D1NAMO) with different sampling frequencies. DTRE achieved forecasting accuracy with a Mean Absolute Relative Difference (MARD) of 7.6% at 30 minutes and 19.2% at 120 minutes, outperforming existing models by 13-41%. For real-time BGL predictions, DTRE surpassed commercial CGM systems with a MARD of 7.17% on the D1NAMO dataset compared to FreeStyle Libre 3 (7.9%) and Dexcom G7 (8.2%). The results confirmed the hypotheses by exceeding clinically acceptable benchmarks in multi - horizon and real-time BGL prediction. Future research should explore integrating additional data elements, incorporating modern wearable devices, Type 2 Diabetes, and expanding datasets to more diverse populations to create accessible and effective diabetes management tools. Ohio

Competition history

JSHS 2025 Category not listed

Resources

National JSHS abstract book

Related projects

ISEF · 2025