ArticleIndian journal of pediatrics2022
A Machine Learning-Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns.
Article in Indian journal of pediatrics, 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, 5 citations in OpenAlex.
- Machine learning-enhanced prediction of fetal growth restriction using fetal cardiac remodeling parameters.BMC medicine · 2025Article
- Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.Nature communications · 2025Review
- The maternal factors during pregnancy for intrauterine growth retardation: An umbrella review.Open medicine (Warsaw, Poland) · 2025Review
- Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review.Frontiers in medicine · 2025Review
- Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intrauterine growth restriction (IUGR) is a condition in which the fetal weight is below the 10th percentile for its gestational age. Prenatal exposure to metals can cause a decrease in fetal growth during gestation thereby reducing birth weight. Therefore, the aim of the present study was to develop a machine learning model for early prediction of IUGR. A total of 126 IUGR and 88 appropriate-for-gestational-age (AGA) samples were collected from the Gynecology Department, Safdarjung Hospital, New Delhi. The predictive models were developed using the Weka software. The models developed using all the features gave the highest accuracy of 95.5% with support vector machine (SMO) algorithm and 88.5% with multilayer perceptron (MLP) algorithm. Further, models developed after feature selection using 14 important and statistically significant variables also gave the highest accuracy of 98.5% with SMO algorithm and 99% with Naïve Bayes (NB) algorithm. The study concluded SMO_31, SMO_14, MLP_31, and NB_14 to be the better classifiers for IUGR prediction.
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Registered trials
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