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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...

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First-Trimester Gestational Diabetes Mellitus Risk Prediction with Machine Learning Techniques: Results from the

Nikolaos Pazaras¹, Antonios Siargkas², Antigoni Tranidou²

¹Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.

Journal of Clinical Medicine

|March 28, 2026

Summary

Machine learning models using first-trimester data show modest ability to predict gestational diabetes mellitus (GDM). Routine clinical factors were as effective as detailed dietary information for early GDM risk assessment.

Keywords:

GDM ML SHAP class imbalance explainable AI gestational diabetes mellitus gradient boosting methods machine learning maternal nutrition micronutrients prediction

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Area of Science:

Obstetrics and Gynecology
Medical Informatics
Epidemiology

Background:

Gestational diabetes mellitus (GDM) poses risks to maternal and fetal health.
Current GDM screening occurs late, limiting early intervention opportunities.
Predicting GDM risk earlier could improve pregnancy outcomes.

Purpose of the Study:

To evaluate machine learning (ML) models for early GDM risk prediction.
To assess the utility of first-trimester clinical and dietary data.
To identify key predictors for GDM development.

Main Methods:

Analysis of 797 pregnant women from the BORN2020 cohort.
Evaluation of ten ML algorithms with five class-imbalance strategies.
Utilized first-trimester demographics, obstetric history, lifestyle, and dietary micronutrient data.

Main Results:

The best ML model achieved an AUC-ROC of 0.664; a reduced nine-feature model using clinical data yielded a higher AUC of 0.712.
Maternal age and pre-pregnancy BMI were the strongest predictors.
Detailed dietary micronutrient data did not enhance prediction accuracy.
Models showed substantial miscalibration, limiting absolute risk estimation.

Conclusions:

First-trimester ML models offer modest discriminative ability for early GDM prediction.
Routine clinical variables performed comparably to detailed dietary assessments.
Models require external validation and recalibration for clinical use, serving as exploratory risk-ranking tools.