ArticleBMC pregnancy and childbirth2021
An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: application in primary health care centres.
Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- 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
- First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis.BJOG : an international journal of obstetrics and gynaecology · 2025Pooled it
- Progress of the application clinical prediction model in polycystic ovary syndrome.Journal of ovarian research · 2023Pooled it
- Pooled it
- Development and external validation of a non-invasive early gestational diabetes mellitus prediction model integrating social network variables: a machine learning-based prospective cohort study.BMC pregnancy and childbirth · 2025Article
- Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China.BMC pregnancy and childbirth · 2025Article
- Machine learning based model for the early detection of Gestational Diabetes Mellitus.BMC medical informatics and decision making · 2025Article
- Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.BMC medical informatics and decision making · 2025Observational
- Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran.BMC medical informatics and decision making · 2025Article
- Artificial Neural Networks to Predict Metabolic Syndrome without Invasive Methods in Adolescents.Journal of clinical medicine · 2024Article
- Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach.Journal of endocrinological investigation · 2024Article
- Article
- Low muscle mass index is associated with type 2 diabetes risk in a Latin-American population: a cross-sectional study.Frontiers in nutrition · 2024Article
- A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery.International journal of women's health · 2024Review
- Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy.BMC pregnancy and childbirth · 2023Article
- MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women.Scientific reports · 2023Article
- Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms.BMJ open diabetes research & care · 2023Article
- A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails.Scientific reports · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGestational diabetes mellitus (GDM) is one of the critical causes of adverse perinatal outcomes. A reliable estimate of GDM in early pregnancy would facilitate intervention plans for maternal and infant health care to prevent the risk of adverse perinatal outcomes. This study aims to build an early model to predict GDM in the first trimester for the primary health care centre.
methodsCharacteristics of pregnant women in the first trimester were collected from eastern China from 2017 to 2019. The univariate analysis was performed using SPSS 23.0 statistical software. Characteristics comparison was applied with Mann-Whitney U test for continuous variables and chi-square test for categorical variables. All analyses were two-sided with p < 0.05 indicating statistical significance. The train_test_split function in Python was used to split the data set into 70% for training and 30% for test. The Random Forest model and Logistic Regression model in Python were applied to model the training data set. The 10-fold cross-validation was used to assess the model's performance by the areas under the ROC Curve, diagnostic accuracy, sensitivity, and specificity.
resultsA total of 1,139 pregnant women (186 with GDM) were included in the final data analysis. Significant differences were observed in age (Z=-2.693, p=0.007), pre-pregnancy BMI (Z=-5.502, p<0.001), abdomen circumference in the first trimester (Z=-6.069, p<0.001), gravidity (Z=-3.210, p=0.001), PCOS (χ
conclusionsThis research developed a simple model to predict the risk of GDM using machine learning algorithm based on pre-pregnancy BMI, abdomen circumference in the first trimester, age, PCOS, gravidity, irregular menstruation, and family history of diabetes. The model was easy in operation, and all predictors were easily obtained in the first trimester in primary health care centres.
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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.