ArticleFrontiers in surgery2022
Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- 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
- ProtoMAP: prototypical network based few-shot learning for missed abortion prediction.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.Journal of clinical ultrasound : JCU · 2025Review
- Prediction of low birth weight from fetal ultrasound and clinical characteristics: a comparative study between a low- and middle-income and a high-income country.BMJ global health · 2024Article
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Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study analyzed the influencing factors of fetal growth restriction (FGR), and selected epidemiological and fetal parameters as risk factors for FGR. Objective: To establish a dynamic prediction model of FGR. Methods: This study used two methods, support vector machine (SVM) and multivariate logistic regression, to establish the prediction model of FGR at different gestational weeks. Results: At 20-24 weeks and 25-29 weeks of gestation, the effect of the multivariate Logistic method on model prediction was better. At 30-34 weeks of gestation, the prediction effect of FGR model using the SVM method is better. The ROC curve area was above 85%. Conclusions: The dynamic prediction model of FGR based on SVM and logistic regression is helpful to improve the sensitivity of FGR in pregnant women during prenatal screening. The establishment of prediction models at different gestational ages can effectively predict whether the fetus has FGR, and significantly improve the clinical treatment effect.
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Registered trials
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