Construction and Clinical Application of a Machine Learning-Based Early Pre-diction Model for Gestational Diabetes Mellitus
10.3969/j.issn.1003-6946.2025.11.011
- VernacularTitle:基于机器学习的妊娠期糖尿病早期预测模型的构建及临床应用
- Author:
Jiaqi LIU
1
;
Jiazhen GAO
;
Yanni MENG
;
Chang WANG
;
Dongying ZHENG
;
Lixia WANG
Author Information
1. 大连医科大学附属第二医院产科,辽宁 大连 116021;东莞市黄江医院妇产科,广东 东莞 523750
- Publication Type:Journal Article
- Keywords:
Gestational diabetes mellitus;
Extreme gradient boosting;
Early prediction;
Clinical decision sup-port system
- From:
Journal of Practical Obstetrics and Gynecology
2025;41(11):915-921
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.
