New References for Neonatal Weight by Gestational Age and Sex, Holguín, Cuba

This work studies the problem of identifying risk factors of Small for Gestational Age (SGA) and building classifiers for SGA prediction. Recently, SGA infants have received more and more concerns as this illness brings many difficulties to them along with their whole life. Some experts have begun to study the risk factors of SGA onset by using traditional statistical ways. Others have used logistic regression (LR) to construct SGA prediction models. Meanwhile, machine learning have evolved and envisioned as a tool able to potentially identify babies with SGA. This work tests several feature selection methods. Based on the risk factors obtained through them, it trains support vector machine, random forest, and LR models and evaluates them via 10-fold cross validation in terms of precision and area under the curve of receiver operator characteristic curve. The results show that sparse LR of the wrapper algorithms owns the best feature selection effectiveness. In addition, this work compares data driven factors and knowledge driven factors and shows that the feature selection is necessary and effective. Among the trained classifiers, the LR model achieves the best performance on the data driven factors.

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The factors affecting the physical development of neonates in pregnant women with or without gestational diabetes mellitus

Zhao

Jia

et al. 2021

PLoS ONE

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Objectives To explore the factors affecting neonatal physical development in pregnant women with or without gestational diabetes mellitus (GDM). Methods The subjects were selected from the pregnant woman giving birth in 2nd Affiliated Hospital of Zhengzhou University, from November 2015 to May 2016. The age, occupation, education level, gestational age, body weight before pregnancy, body weight at delivery, body height, delivery pattern, GDM status of pregnant women and neonatal gender, birth weight (BW), chest circumference (CC), head circumference (HC) and birth length (BL) were collected through medical records and questionnaires. The clinical data were retrospectively analyzed and studied. Results The significant differences were found between women with GDM and without GDM in following neonatal variables (P<0.05): BW, CC, and HC. GDM status increased the incidence of macrosomia (OR = 2.241, 95% CI: 1.406–3.573), large CC (OR = 2.470, 95% CI: 1.687–3.6153). Gestational weight gain (GWG) above IOM guideline was risk factor for macrosomia (OR = 1.763, 95% CI:1.098–2.833), large HC (OR = 1,584, 95% CI: 1.093–2.296) and large CC (OR = 1.707, 95% CI:1.163–2.506). Underweight was risk factor for short BL (OR = 2.543, 95% CI:1.161–5.571) and small CC (OR = 1.901, 95% CI:1.064–3.394). Female neonate was prone to appear short BL(OR = 2.831, 95% CI: 1.478–5.422) and small HC (OR = 2.750, 95% CI: 1.413–5.350), and not likely to macrosomia (OR = 0.538, 95% CI: 0.343–0.843), longer BL (OR = 0.584, 95% CI: 0.401–0.850), large HC (OR = 0.501, 95% CI: 0.352–0.713), and (OR = 0.640, 95% CI: 0.446–0.917). For women with GDM, gestational age was an risk factor of neonatal BW (low BW: OR = 0.207, 95% CI: 0.085–0.503; macrosomia: OR = 1.637, 95% CI: 1.177–2.276), BL (short BL: OR = 0.376, 95% CI: 0.241–0.585; long BL: OR = 1.422, 95% CI: 1.054–1.919), HC (small HC: OR = 0.343, 95% CI: 0.202–0.583; large HC: OR = 1.399, 95% CI: 1.063–1.842) and CC (small CC: OR = 0.524, 95% CI: 0.374–0.733; large CC: OR = 1.485, 95% CI: 1.138–1.936). Conclusions In our study, gestational age, GDM status, neonatal gender, GWG and pre-pregnancy body mass index (BMI) are associated the abnormal physical development of neonates. In women with GDM, gestational age was correlate with neonatal abnormal physical developments.

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New References for Neonatal Weight by Gestational Age and Sex, Holguín, Cuba

Abstract: How to citeComplete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative

Cited by 4 publications

References 24 publications

An Efficient Small for Gestational Age Prognosis System Using Stacked Generalization Scheme (SGS)

An Efficient Small for Gestational Age Prognosis System Using Stacked Generalization Scheme (SGS)

A data-driven approach to predict Small-for-Gestational-Age infants

The factors affecting the physical development of neonates in pregnant women with or without gestational diabetes mellitus

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