Continuous glucose monitoring using machine learning models and IoT device data: A meta-analysis

Abstract

BACKGROUND: Machine learning offers diverse options for effectively managing blood glucose levels in diabetes patients. Selecting the right ML algorithm is critical given the array of available choices. Integrating data from IoT devices presents promising opportunities to enhance real-time blood glucose management models. OBJECTIVE: This meta-analysis aims to evaluate the effectiveness of machine learning models utilizing IoT device data for predicting blood glucose levels. METHODS: We systematically searched electronic databases for studies published between 2019 and 2023. We excluded studies lacking ML model derivation or performance metrics. The Quality Assessment of Diagnostic Accuracy Studies tool assessed study quality. Our primary outcomes compared ML models for BG level prediction across different prediction horizons (PHs). RESULTS: We analyzed ten eligible studies across prediction horizons of 15, 30, 45, and 60 minutes. ML models exhibited mean absolute RMSE values of 15.02 (SD 1.45), 21.488 (SD 2.92), 30.094 (SD 3.245), and 35.89 (SD 6.4) mg/dL, respectively. Random Forest demonstrated superior performance across these PHs. CONCLUSION: We observed significant heterogeneity across all subgroups, indicating diverse sources of variability. As the PH lengthened, the RMSE for blood glucose prediction by the ML model increased, with Random Forest showing the highest relative performance among the ML models.