Hung SM, Chen CP, Sun FJ, Chen YY, Wang LK, Chen CY. Early risk stratification of gestational diabetes using interpretable machine learning with first-trimester screening parameters. World J Diabetes 2026; 17(9): 123276 [DOI: 10.4239/wjd.123276]
Corresponding Author of This Article
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
Research Domain of This Article
Obstetrics & Gynecology
Article-Type of This Article
research-article
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Hung SM, Chen CP, Sun FJ, Chen YY, Wang LK, Chen CY. Early risk stratification of gestational diabetes using interpretable machine learning with first-trimester screening parameters. World J Diabetes 2026; 17(9): 123276 [DOI: 10.4239/wjd.123276]
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 23.7 Hours
Abstract
BACKGROUND
Gestational diabetes mellitus (GDM) is usually diagnosed at 24-28 weeks of gestation, when opportunities for early prevention may already be limited. First-trimester maternal characteristics and placental biomarkers may provide earlier risk information, but their combined predictive value remains insufficiently defined.
AIM
To develop and evaluate an interpretable machine learning approach for first-trimester risk stratification of GDM.
METHODS
This retrospective cohort study included singleton pregnancies undergoing first-trimester screening at MacKay Memorial Hospital, a tertiary referral center, between January 2019 and July 2024. Maternal characteristics, obstetric and medical history, mean arterial pressure, ultrasound parameters, and biochemical markers were used to train eight machine learning algorithms. Class imbalance was addressed using synthetic minority oversampling, random oversampling, and random under-sampling. Model performance was evaluated on an independent test set, and interpretability was assessed using SHapley Additive exPlanations (SHAP) and patient-level heatmaps.
RESULTS
Among 2756 singleton pregnancies, 352 women developed GDM (12.8%). Women who developed GDM were older and had higher pregestational body mass index and mean arterial pressure than those without GDM. First-trimester placental biomarkers, including pregnancy-associated plasma protein A, placental growth factor, and free β-human chorionic gonadotropin, were significantly lower in women who subsequently developed GDM. Among all evaluated models and resampling strategies, gradient boosting with random oversampling achieved the best overall performance, with an area under the receiver operating characteristic curve of 0.768, sensitivity of 0.647, specificity of 0.777, positive predictive value of 0.289, negative predictive value of 0.940, and F1 score of 0.400. SHAP analysis identified maternal age, mean arterial pressure, pregestational weight, pregnancy-associated plasma protein A, and placental growth factor as the major contributors to model predictions.
CONCLUSION
An interpretable machine learning approach integrating first-trimester clinical, obstetric, and biochemical parameters may serve as a potential early risk stratification tool for GDM. Given its favorable rule-out performance, the model may help identify women at relatively low risk of GDM during early pregnancy. Further external validation is required before broader clinical implementation.
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.
