Healthcare IoT-Enabled PMAFNet: A Progressive Multimodal Adaptive Fusion Network for Understanding HbA1c Fluctuations

The Internet of Things (IoT) has transformed healthcare via Healthcare-IoT (HIoT) by enabling continuous patient monitoring and efficient data analytics, offering promising solutions for managing the escalating global diabetes burden. The glycated hemoglobin (HbA1c) is an important biomarker for diabetes evaluation and control. Accurate prediction of HbA1c fluctuations remains a critical challenge, as existing models often overlook the interplay between stable patient profiles (demographics, lifestyle, clinical indicators) and dynamic dietary nutrient intake, limiting their ability to model glycemic responses. This study constructs a comprehensive multi-source dataset comprising detailed personal information, lifestyle, medical tests, and multi-day dynamic nutrient intake through the HIoT system. Utilizing this dataset, we propose a novel progressive multimodal adaptive fusion network (PMAFNet) to decode the complex determinants of HbA1c variability. PMAFNet employs two core modules: the multimodal graph feature-level enhancement (MGFE) module processes static and dynamic features to capture structural dependencies and temporal patterns, and the adaptive modality-aware progressive fusion (AMPF) module integrates these representations via the hierarchical attention mechanism. Experimental results demonstrate that PMAFNet achieves 95.24% accuracy. Ablation analysis confirms nutrient intake is a critical driver, with accuracy declining by 19.04% upon its exclusion. Furthermore, the SHapley Additive exPlanations (SHAP) was employed to interpret the PMAFNet and identify key features influencing HbA1c. This study highlights the significance of incorporating multimodal data to unravel the complexity of glycemic regulation and reveals the pivotal influence of nutrient intake in diabetes management.