Graph Convolutional Network Enabled Two-Stream Learning Architecture for Diabetes Classification based on Flash Glucose Monitoring Data

• Published in: Biomedical signal processing and control Vol. 69; p. 102896
• Main Authors: Liu, Yicun, Liu, Wei, Chen, Haorui, Cai, Xiaoling, Zhang, Rui, An, Zhe, Shi, Dawei, Ji, Linong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2021
• Subjects: Attention mechanism; Data processing; Diabetes classification; Graph convolutional network; LSTM; Attention mechanism; Data processing; Graph convolutional network; LSTM; Diabetes classification
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102896

•A graph convolutional network enabled two-stream learning architecture is proposed for diabetes classification.•The effectiveness of the proposed method is demonstrated through clinical data.•The results indicate the feasibility of achieving diabetes classification by learning the information patterns hidden in continuous glucose monitoring data. The classification of type 1 and type 2 diabetes is currently performed based on biochemical indicators and clinical experience. However, considering the unsatisfactory efficiency and accuracy of the experience-based diabetes type classification, we aim to propose a data-driven diabetes classification model through exploiting features contained in flash glucose monitoring (FGM) data. In particular, we propose a novel data reorganization and topologization method to reasonably extract the features of glycemic variability influence. Furthermore, a graph convolutional network is adopted to learn the inter-day influence feature and a Long Short-Term Memory network to characterize intra-day glycemic variability, which enables simultaneous characterization of slow and fast dynamics in FGM data. Finally, to visualize the effectiveness of our model, a t-distributed stochastic neighbor embedding method is implemented. The effectiveness of the proposed model is evaluated through a cross-validation approach using a dataset containing FGM records of 113 diabetic subjects. Compared with classical machine learning algorithms and neural networks, the proposed model achieved the highest specificity value (0.9943) in diabetes type classification, F-Measure (0.8824) and Matthews correlation coefficient score (0.8250). The obtained results indicate the feasibility of achieving diabetes classification by learning the patterns hidden in continuous glucose monitoring data.