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World J Diabetes. Sep 15, 2026; 17(9): 123276
Published online Sep 15, 2026. doi: 10.4239/wjd.123276
Published online Sep 15, 2026. doi: 10.4239/wjd.123276
Early risk stratification of gestational diabetes using interpretable machine learning with first-trimester screening parameters
Shuo-Mei Hung, Chie-Pein Chen, Yi-Yung Chen, Liang-Kai Wang, Chen-Yu Chen, Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei 104217, Taiwan
Fang-Ju Sun, Department of Medical Research, MacKay Memorial Hospital, Taipei 104217, Taiwan
Chen-Yu Chen, Department of Medicine, MacKay Medical University, New Taipei City 252005, Taiwan
Author contributions: Hung SM and Chen CY designed the study; Hung SM drafted the manuscript; Chen CP and Sun FJ performed the formal analysis and contributed to methodology and software; Chen YY and Wang LK were responsible for data curation; Chen CY supervised the study and critically revised the manuscript for important intellectual content; all authors have read and approved the final manuscript.
AI contribution statement: AI tools (specifically ChatGPT) were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Supported by the National Science and Technology Council of Taiwan, No. NSTC 113-2314-B-195-016-MY3.
Institutional review board statement: This study was approved by the Institutional Review Board of MacKay Memorial Hospital (No. 26MMHIS097e).
Informed consent statement: The requirement for written informed consent was waived by the Institutional Review Board because of the retrospective study design based on review of medical records. All personal identifiers were anonymized prior to analysis.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
Corresponding author: Chen-Yu Chen, MD, Full Professor, Department of Obstetrics and Gynecology, MacKay Memorial Hospital, No. 92, Section 2 Zhongshan North Road, Taipei 104217, Taiwan. f122481@mmh.org.tw
Received: May 13, 2026
Revised: June 15, 2026
Accepted: July 8, 2026
Published online: September 15, 2026
Processing time: 109 Days and 23.3 Hours
Revised: June 15, 2026
Accepted: July 8, 2026
Published online: September 15, 2026
Processing time: 109 Days and 23.3 Hours
Core Tip
Core Tip: This retrospective cohort study developed an interpretable machine learning model for early risk stratification of gestational diabetes mellitus using first-trimester clinical and biomarker parameters. In 2756 pregnancies, gradient boosting with random oversampling achieved moderate discrimination with high negative predictive value. Model interpretation using SHapley Additive exPlanations and patient-level heatmaps identified maternal and placental factors as key contributors. This approach may support early identification of at-risk women and facilitate targeted preventive strategies before routine mid-pregnancy screening.