Faculty of Health Sciences, Department of Gynecology and Obstetrics Nursing, Eskişehir Osmangazi University, Eskişehir, Turkey.
Hoşnudiye Mah. Ayşen Sokak Dorya Rezidans, A Blok no:28/77, Eskişehir, Turkey.
BMC Pregnancy Childbirth. 2024 Aug 31;24(1):574. doi: 10.1186/s12884-024-06783-7.
We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.
We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model.
97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively.
The XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries.
本研究旨在确定在受孕前阶段，使用社会人口学和产科特征预测妊娠期糖尿病（GDM）的表现最佳的基于机器学习（ML）算法。
我们收集了在孕早期就诊于产科诊所的孕妇数据。评估了产妇年龄、体重指数、孕次、产次、既往出生体重、吸烟状况、首诊静脉血浆葡萄糖水平、糖尿病家族史以及口服葡萄糖耐量试验结果。根据是否患有 GDM 以及是否为初产妇或经产妇将患者分为不同组。使用 7 种常见的 ML 算法构建预测模型。
本研究共纳入 97 名母亲。19 名初产妇患有 GDM，26 名初产妇未患有 GDM。29 名经产妇患有 GDM，23 名经产妇未患有 GDM。结果发现，静脉血浆葡萄糖水平、产妇 BMI 和糖尿病家族史是最重要的特征变量。极端梯度提升（XGB）分类器在两个模型中的预测价值最高，准确率分别为 66.7%和 72.7%。
使用产妇社会人口学发现和产科史构建的 XGB 分类器模型可以作为 GDM 的早期预测模型，特别是在低收入国家。
