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

Abstract

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.