Advanced Machine Learning did not Surpass Traditional Logistic Regression in First-Trimester Gestational Diabetes Mellitus Prediction - A Retrospective Single-Center Study From Eastern China

Aug 2, 2026

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Gestational diabetes mellitus (GDM) creates serious risks for mothers and fetuses, yet effective early prediction tools remain limited. This retrospective single-center study used 2023 medical examination data from 956 singleton pregnancies in Pinghu City, building and comparing traditional logistic regression with six advanced machine learning models. Model discrimination, calibration, and clinical utility were evaluated using ROC, precision-recall, calibration curves, Hosmer-Lemeshow testing, and decision curve analysis, finding logistic regression achieved the best AUC and good calibration.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Study background and diagnostic challenges

Methods

Study design and data source

Feature selection and model building

Evaluation metrics

International Journal of General Medicine downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. International Journal of General Medicine Open Access Full Text Article ORIGINAL RESEARCH Advanced Machine Learning did not Surpass Traditional Logistic Regression in First-Trimester Gestational Diabetes Mellitus Prediction: A Retrospective Single-Center Study From Eastern China Hongyan Ni 1 , Jinli Miao2 , Jian Chen 3 1Department of maternity care, PingHu Maternal and Child Health Hospital, Jiaxing, Zhejiang, 314200, People’s Republic of China; 2The Yangtze River Delta Biological Medicine Research and Development Center of Zhejiang Province, Yangtze Delta Region Institution of Tsinghua University, Hangzhou, Zhejiang, 314006, People’s Republic of China; 3Department of internal medicine, PingHu Maternal and Child Health Hospital, Jiaxing, Zhejiang, 314200, People’s Republic of China Correspondence: Jian Chen, Email [15988398470@163.com](15988398470@163.com) | Background: Gestational diabetes mellitus (GDM) poses serious health risks to both mothers and fetuses. However, effective tools for identifying GDM are lacking. This study, based on a Chinese cohort, aims to construct and compare the predictive performance of traditional logistic regression (LR) and six advanced machine learning (ML) models, thereby aiding in the early identification and intervention of GDM.
Methods: This retrospective study utilized medical examination data from 956 singleton pregnant women collected between January and December 2023 from ten maternal and child health hospitals in Pinghu City. We employed receiver operating characteristic curvesand precision-recall curves to assess the predictive performance of the models. Decision curve analysis (DCA) was used to evaluate clinical utility, while calibration curves and Hosmer-Lemeshow (HL) tests were applied to assess the calibration of each model. Results: The 956 participants were randomly divided into a training set and a validation set at a 3:1 ratio. We identified 13 features through Spearman correlation analysis and the Boruta algorithm to construct the models. The LR model exhibited the best AUC at 0.787 (0.723–0.85), outperforming the seven other ML models including RF at 0.776 (0.711–0.841) . Furthermore, the LR model showed good calibration and clinical utility.
Conclusion: Although ML has tremendous potential, in predicting the occurrence of GDM based on common early pregnancy data, the ML models did not completely outperform the traditional LR model. Simpler, traditional models may be more effective than complex ML approaches.
Keywords: GESTATIONAL diabetes mellitus, logistic regression, machine learning, first trimester, prediction model | | --- | | Introduction
Gestational diabetes mellitus (GDM) is a metabolic syndrome characterized by abnormal elevations in blood glucose levels during pregnancy. Although it typically resolves after childbirth, GDM poses significant short-term and long-term health risks to both mother and child.1 The incidence of GDM exhibits substantial variation across different populationsand has shown a consistent upward trend.2 In the United States, the prevalence of GDM increased from 4.6% to 8.2% between 2006 and 2016, representing a 78% relative increase. This rise was particularly pronounced among Hispanic, non-Hispanic Black women, and women of other races/ethnicities compared to non-Hispanic White women.3 The observed disparities in GDM susceptibility across different racial groups can be attributed to a combination of genetic predisposition, lifestyle factors, and socioeconomic determinants, which contribute to significant variations in incidence | Received: 18 December 2024 Accepted: 19 April 2025 Published: 26 April 2025 International Journal of General Medicine 2025:18 2263–2274 2263 © 2025 Ni et al. This work is published and licensed by Dove Medical Press Limited. The full terms of

FAQs

What was the main goal of the study?

To construct and compare the predictive performance of traditional logistic regression and six advanced machine learning models for first-trimester GDM prediction using early pregnancy data.

How were the models evaluated in this research?

Discrimination was assessed using ROC and precision-recall curves, while calibration used calibration curves and Hosmer-Lemeshow tests. Clinical utility was evaluated with decision curve analysis.

Did advanced machine learning models outperform logistic regression?

No. Logistic regression showed the best AUC and demonstrated good calibration and clinical utility, while the ML models did not completely surpass it.

Aug 2, 2026

Upload by Miles