ArticleBMC pregnancy and childbirth2024
The early prediction of gestational diabetes mellitus by machine learning models.
Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.Scientific reports · 2026Article
- Predictive performance of artificial intelligence algorithms for gestational diabetes mellitus in pregnant women: a protocol for systematic review and meta-analysis.Systematic reviews · 2026Article
- Development of machine learning models for early prediction of small for-gestational-age births using maternal sociodemographic and obstetric data.BMC pregnancy and childbirth · 2026Article
- Construction of an interpretable machine learning model for predicting gestational diabetes mellitus based on 45 dietary nutrients.BioData mining · 2026Article
- Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Early-pregnancy trunk phase angle derived from bioelectrical impedance analysis for the prediction of gestational diabetes mellitus.Frontiers in endocrinology · 2026Article
- Article
- A new extended belief rule base method based on neighborhood covering reduction for diabetes diagnosis.PloS one · 2026Article
- Identifying Diabetic Kidney Disease in Type 2 Diabetes Patients Using Explainable Machine Learning: A Case-Control Study.Journal of diabetes research · 2026Article
- Advances in gestational diabetes mellitus screening: Emerging trends and future directions.World journal of diabetes · 2025Review
- Review
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe 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.
methodsWe 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.
results97 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. DISCUSSION: 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.