Journal Article

Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study

Allen, Alexander C., Maguire, Bryan, Hamilton, David, Zhang, Hongqun, Allen, Victoria M., Kuhle, Stefan, Joseph, K. S.

2018

Overview

Background While 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. Methods Data 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. Results 7.9% 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. Conclusions The 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.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Accuracy / Adult / Age / Artificial intelligence / Birth weight / Body Mass Index / Cohort analysis / Cohort Studies / delivery / Female / Fetal Development / Fetal growth restriction / Fetal macrosomia / Fetal Macrosomia - epidemiology / Fetuses / Gestational Weight Gain / Gynecology / Health care policy / Health risk assessment / Humans / Hypoglycemia / Infant / Infant, Newborn / Infant, Small for Gestational Age / Labor / Logistic Models / Machine Learning / Maternal and Child Health / Medicine / Medicine & Public Health / Neural networks / Neural Networks (Computer) / Nova Scotia - epidemiology / postpartum health / Prediction / Pregnancy / Pregnancy Trimester, Second / Prenatal development / Reproductive Medicine / Research Article / Retrospective Studies / Smoking - epidemiology / Statistics as Topic / Womens health / Young Adult