Previously submitted to: JMIR Diabetes (no longer under consideration since Apr 17, 2026)
Date Submitted: Apr 9, 2026
Open Peer Review Period: Apr 17, 2026 - Apr 17, 2026
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Early Prediction of Gestational Diabetes Mellitus Using Machine Learning: A Random Forest, Logistic Regression, and XGBoost Approach
ABSTRACT
Background:
Gestational diabetes mellitus (GDM) is a pregnancy-related complication that can result in serious maternal and fetal outcomes if not diagnosed early. Conventional diagnostic methods, such as oral glucose tolerance tests (OGTT), detect GDM later in pregnancy, delaying timely intervention.
Objective:
This study aims to develop an interpretable machine learning framework for early prediction of GDM using routinely collected clinical features, improving the reliability and timeliness of clinical decision-making.
Methods:
A clinical dataset of 1,012 pregnant women was analyzed, including features such as age, number of pregnancies, weight, height, BMI, and family history of diabetes. Missing values were imputed, and class imbalance was addressed via oversampling. Three machine learning models—Random Forest (RF), Logistic Regression (LR), and XGBoost (XGB) were implemented to classify cases as GDM or non-GDM. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Feature importance was analyzed to ensure clinical interpretability.
Results:
Random Forest outperformed other models with an accuracy of 0.89, precision of 0.78, recall of 0.70, F1-score of 0.74, and ROC-AUC of 0.884. Logistic Regression achieved lower recall (0.52) and F1-score (0.62), while XGBoost showed moderate performance (F1-score 0.70). Feature importance analysis identified BMI, age, weight, and number of pregnancies as the most predictive variables, consistent with clinical knowledge. Low false-negative rates in Random Forest indicate high clinical reliability for early GDM detection.
Conclusions:
The proposed Random Forest-based framework demonstrates high discriminative ability, interpretability, and reproducibility for early GDM prediction. Its integration into clinical decision support systems could enable timely interventions, personalized monitoring, and improved maternal and fetal outcomes. External validation on multi-center datasets and incorporation of additional clinical and lifestyle variables are recommended to enhance generalizability. Clinical Trial: Not applicable.
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