Blood glucose prediction for type 2 diabetes using clustering-based domain adaptation

• Published in: Biomedical signal processing and control Vol. 105; p. 107629
• Main Authors: Yang, Tao, Yu, Xia, Tao, Rui, Li, Hongru, Zhou, Jian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2025
• Subjects: Clustering algorithm; Deep learning; Domain adaptation; Glucose prediction; Transfer learning; Type 2 diabetes; Deep learning; Type 2 diabetes; Domain adaptation; Glucose prediction; Transfer learning; Clustering algorithm
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2025.107629

•Clustering-based domain adaptation for glucose prediction of T2D with limited data.•Subgroup clustering enhances domain adaptability via source domain screening.•Adaptation network reduces distribution mismatch in cross-subject predictions.•The method was validated on a clinical dataset of 908 T2D and a public dataset.•The method exhibits favorable glucose prediction and long-term transfer capability. For patients with type 2 diabetes (T2D), accurate prediction of blood glucose variations is essential for maintaining glycemic control, decreasing the occurrence of hypoglycemic and hyperglycemic events, and preventing diabetes complications. However, this is difficult to achieve due to insufficient early glucose data and the complexity of glucose dynamics. Additionally, the high variability among individuals poses challenges for data transfer between patients. In this work, a clustering-based domain adaptation method is proposed for personalized glucose prediction of T2D with insufficient data. Firstly, the multi-level clustering method is used to subtype the heterogeneous group of patients with T2D into multiple homogenous subgroups to deal with the high inter-individual variability. Then, a domain adaptation prediction network is designed to overcome the challenges caused by insufficient historical data of the target patient through cross-patient knowledge transfer and obtain a personalized deep prediction model suitable for the target patient. The effectiveness of the proposed method was evaluated in a clinical dataset containing continuous glucose monitoring (CGM) measurement records from 908 patients with T2D, each with only a small amount of data. The 30-minute prediction horizon achieved an average root mean square error of 14.96 mg/dL, with over 94 % of predictions clinically accurate. In addition, we evaluated the long-term transferability of the proposed method on the publicly available ShanghaiT2DM Dataset and compared it with the state-of-the-art (SOTA) methods. The results demonstrate that the proposed personalized method can achieve accurate glucose prediction for patients with T2D, even with only one day of historical CGM records available.