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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References
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.
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.
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.
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.
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.
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.
Acknowledgements
The authors acknowledge ICMR for providing facilities to perform the research work in Electron Microscopy & Environmental Toxicology Lab, National Institute of Pathology, New Delhi. The authors also acknowledge Deepanshu Saxena for his support and guidance in machine learning approach.
Funding
Indian Council of Medical Research (Award No. ISRM/12(49)/2019).
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DP, RD, and AKJ conceptualized the study; PS implemented the bioinformatics part and drafted the manuscript; AKM and RD gave insight on result interpretation; AKJ supervised the whole process. All the authors have read and agreed to the final version of the manuscript. AKJ will act as the guarantor for this paper.
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Ravi Deval and Pallavi Saxena contributed equally to this work.
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Deval, R., Saxena, P., Pradhan, D. et al. A Machine Learning–Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns. Indian J Pediatr 89, 1140–1143 (2022). https://doi.org/10.1007/s12098-022-04273-2
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DOI: https://doi.org/10.1007/s12098-022-04273-2