ArticleBMC pregnancy and childbirth2018
Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study.
Article in BMC pregnancy and childbirth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 176 citations in OpenAlex.
- Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review.BMC pregnancy and childbirth · 2025Pooled 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
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Evaluating the Performance of Traditional Pharmacoepidemiologic and Machine Learning Models to Predict Pregnancies at Risk of Major Congenital Malformations.Birth defects research · 2026Article
- Artificial Intelligence for Neonatal and Perinatal Mortality Prevention: A Systematic Review of Machine Learning and Deep Learning Applications.Healthcare (Basel, Switzerland) · 2026Review
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Machine learning to improve the prediction of Large for Gestational Age (LGA) neonates: a cohort study.BMC pregnancy and childbirth · 2026Article
- Prediction of low 5-minute Apgar scores: development and internal validation of parity-stratified clinical prediction models for sub-Saharan Africa.BMC pregnancy and childbirth · 2026Article
- Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Lipid metabolism-based machine learning models for predicting large for gestational age in non-diabetic pregnancies.Frontiers in endocrinology · 2026Article
- Antepartum prediction of shoulder dystocia using machine learning.Archives of gynecology and obstetrics · 2025Article
- Machine learning vs. traditional logistic regression: predictive performance and risk factor identification for child nutritional outcome in Pakistan.BMC public health · 2025Article
- Explainable machine learning model for predicting septic shock in critically sepsis patients based on coagulation indexes: A multicenter cohort study.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2025Article
- Development and validation of super learner models to predict small and large for gestational age in the second generation.Scientific reports · 2025Article
- Predicting Fetal Growth with Curve Fitting and Machine Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model.JMIR pediatrics and parenting · 2025Article
- Utilization of tree-based machine learning models for predicting low birth weight cases.BMC pregnancy and childbirth · 2025Article
- An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection.PloS one · 2025Article
- Antenatal prediction of small for gestational age at birth based on four birthweight standards using machine learning algorithms.Frontiers in artificial intelligence · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWhile there is increasing interest in identifying pregnancies at risk for adverse outcome, existing prediction models have not adequately assessed population-based risks, and have been based on conventional regression methods. The objective of the current study was to identify predictors of fetal growth abnormalities using logistic regression and machine learning methods, and compare diagnostic properties in a population-based sample of infants.
methodsData for 30,705 singleton infants born between 2009 and 2014 to mothers resident in Nova Scotia, Canada was obtained from the Nova Scotia Atlee Perinatal Database. Primary outcomes were small (SGA) and large for gestational age (LGA). Maternal characteristics pre-pregnancy and at 26 weeks were studied as predictors. Logistic regression and select machine learning methods were used to build the models, stratified by parity. Area under the curve was used to compare the models; relative importance of predictors was compared qualitatively.
results7.9% and 13.5% of infants were SGA and LGA, respectively; 48.6% of births were to primiparous women and 51.4% were to multiparous women. Prediction of SGA and LGA was poor to fair (area under the curve 60-75%) and improved with increasing parity and pregnancy information. Smoking, previous low birthweight infant, and gestational weight gain were important predictors for SGA; pre-pregnancy body mass index, gestational weight gain, and previous macrosomic infant were the strongest predictors for LGA.
conclusionsThe machine learning methods used in this study did not offer any advantage over logistic regression in the prediction of fetal growth abnormalities. Prediction accuracy for SGA and LGA based on maternal information is poor for primiparous women and fair for multiparous women.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.