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  3. An Explainable Machine Learning Model For Predicting Preterm Birth In Pregnant Women With Gestational Diabetes Mellitus And Hypertensive Disorders Of Pregnancy: Development And External Validation.
  1. Home
  3. An Explainable Machine Learning Model For Predicting Preterm Birth In Pregnant Women With Gestational Diabetes Mellitus And Hypertensive Disorders Of Pregnancy: Development And External Validation.

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An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes

Landan Kang1, Dan Luo2, Wenchi Xie2

  • 1School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Frontiers in Endocrinology
|December 4, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Elastic Net regressionShapley Additive Explanationsgestational diabetes mellitushypertensive disorders of pregnancypreterm birthrisk prediction model

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A new machine learning model accurately predicts preterm birth risk in high-risk pregnancies with gestational diabetes mellitus and hypertensive disorders. This tool aids in individualized obstetric risk management for better patient outcomes.

Area of Science:

  • Obstetrics and Gynecology
  • Machine Learning in Healthcare
  • Perinatal Medicine

Background:

  • Gestational diabetes mellitus (GDM) and hypertensive disorders of pregnancy (HDP) frequently coexist, sharing risk factors like insulin resistance and endothelial dysfunction.
  • This comorbidity significantly elevates the risk of preterm birth, necessitating targeted predictive strategies.
  • Existing predictive models often overlook this specific high-risk pregnancy cohort.

Purpose of the Study:

  • To develop and externally validate a machine learning model for predicting preterm birth in pregnancies with comorbid GDM and HDP.
  • To assess the clinical utility and interpretability of the developed predictive model.
  • To identify key predictors of preterm birth in this high-risk population.

Main Methods:

  • Retrospective dual-center study utilizing electronic medical records from 121 (development) and 136 (validation) pregnant women.
  • Application of machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF), and Naive Bayes (NB).
  • Utilized Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance and Shapley Additive Explanations (SHAP) for model interpretability.

Main Results:

  • Elastic Net regression identified five key predictors: ALT, AST, Albumin, LDH, and systolic blood pressure at 32-36 weeks.
  • The Naive Bayes (NB) model demonstrated superior clinical utility (NRI, IDI) and robustness compared to LASSO and RF.
  • External validation confirmed strong generalization, with an AUC of 0.777, accuracy of 0.801, sensitivity of 0.792, and specificity of 0.804.

Conclusions:

  • The Naive Bayes model offers robust predictive capabilities and interpretability for preterm birth risk in pregnancies with GDM and HDP.
  • This model can serve as a transparent and clinically applicable tool for personalized obstetric risk management.
  • The findings support the use of machine learning for identifying high-risk pregnancies and optimizing clinical decision-making.