Early gestational diabetes mellitus risk predictor using neural network with NearMiss

Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective preven...

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Bibliographic Details
Published in:Gynecological endocrinology Vol. 41; no. 1; p. 2470317
Main Authors: Zhao, Min, Su, Xiaojie, Huang, Lihong
Format: Journal Article
Language:English
Published: England Taylor & Francis Group 01.12.2025
Subjects:
Adult
Diabetes, Gestational - diagnosis
Diabetes, Gestational - epidemiology
Female
Gestational diabetes mellitus
Glucose Tolerance Test
Humans
logistic regression
NearMiss
neural network algorithms
Neural Networks, Computer
Pregnancy
Risk Assessment - methods
Risk Factors
Gestational diabetes mellitus
logistic regression
NearMiss
neural network algorithms
ISSN:0951-3590, 1473-0766, 1473-0766
Online Access:Get full text
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Summary:Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages. The primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy. Initially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators. As a result, we identified several factors in early pregnancy significantly associated with GDM (  < 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy. In this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention.
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ISSN:0951-3590
1473-0766
1473-0766
DOI:10.1080/09513590.2025.2470317