A Machine Learning-Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns.
Heavy metals
Hormones
IUGR
Machine learning
Thyroid
Journal
Indian journal of pediatrics
ISSN: 0973-7693
Titre abrégé: Indian J Pediatr
Pays: India
ID NLM: 0417442
Informations de publication
Date de publication:
11 2022
11 2022
Historique:
received:
23
09
2021
accepted:
05
05
2022
revised:
28
04
2022
pubmed:
9
8
2022
medline:
19
10
2022
entrez:
8
8
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35941474
doi: 10.1007/s12098-022-04273-2
pii: 10.1007/s12098-022-04273-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1140-1143Informations de copyright
© 2022. The Author(s), under exclusive licence to Dr. K C Chaudhuri Foundation.
Références
Unterscheider J, O’Donoghue K, Daly S, et al. Fetal growth restriction and the risk of perinatal mortality-case studies from the multicentre PORTO study. BMC Pregnancy Childbirth. 2014;14:63.
doi: 10.1186/1471-2393-14-63
Krishna U, Bhalerao S. Placental insufficiency and fetal growth restriction. J Obstet Gynaecol India. 2011;61:505–11.
Malhotra A, Allison BJ, Castillo-Melendez M, Jenkin G, Polglase GR, Miller SL. Neonatal morbidities of fetal growth restriction: pathophysiology and impact. Front Endocrinol (Lausanne). 2019;10:55.
doi: 10.3389/fendo.2019.00055
Zhang J, Xu J, Hu X, et al. Diagnostic method of diabetes based on support vector machine and tongue images. Biomed Res Int. 2017;2017:7961494.
pubmed: 28133611
pmcid: 5241479
Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. machine learning in fetal cardiology: what to expect. Fetal Diagn Ther. 2020;47:363–72.
doi: 10.1159/000505021
Kumar SN, Saxena P, Patel R, et al. Predicting risk of low birth weight offspring from maternal features and blood polycyclic aromatic hydrocarbon concentration. Reprod Toxicol. 2020;94:92–100.
doi: 10.1016/j.reprotox.2020.03.009
Sharma A, Gupta P, Kumar R, Bhardwaj A. dPABBs: A Novel in silico approach for predicting and designing anti-biofilm peptides. Sci Rep. 2016;6:21839.
doi: 10.1038/srep21839