Kapoor Yagyesh, Hasija Yasha
Technol Health Care. 2025;33(1):577-591. doi: 10.3233/THC-241403.
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.
This meta-analysis aims to evaluate the effectiveness of machine learning models utilizing IoT device data for predicting blood glucose levels.
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).
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.
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.
机器学习为有效管理糖尿病患者的血糖水平提供了多种选择。鉴于有一系列可用选择，选择合适的机器学习算法至关重要。整合来自物联网设备的数据为增强实时血糖管理模型带来了有前景的机会。
本荟萃分析旨在评估利用物联网设备数据的机器学习模型预测血糖水平的有效性。
我们系统地在电子数据库中搜索2019年至2023年发表的研究。我们排除了缺乏机器学习模型推导或性能指标的研究。诊断准确性研究的质量评估工具评估研究质量。我们的主要结果比较了不同预测期（PHs）下用于血糖水平预测的机器学习模型。
我们分析了跨越15、30、45和60分钟预测期的十项合格研究。机器学习模型的平均绝对均方根误差值分别为15.02（标准差1.45）、21.488（标准差2.92）、30.094（标准差3.245）和35.89（标准差6.4）mg/dL。随机森林在这些预测期内表现出卓越的性能。
我们在所有亚组中观察到显著的异质性，表明存在多种变异来源。随着预测期延长，机器学习模型预测血糖的均方根误差增加，随机森林在机器学习模型中表现出最高的相对性能。
