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

Journal: BMC pregnancy and childbirth
PMID: 30111303

Abstract

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.

Authors

  • Stefan Kuhle
    Perinatal Epidemiology Research Unit, Departments of Obstetrics & Gynaecology and Pediatrics, Dalhousie University, Halifax, NS, Canada. stefan.kuhle@dal.ca.
  • Bryan Maguire
    Perinatal Epidemiology Research Unit, Departments of Obstetrics & Gynaecology and Pediatrics, Dalhousie University, Halifax, NS, Canada.
  • Hongqun Zhang
    Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada.
  • David Hamilton
    Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada.
  • Alexander C Allen
    Perinatal Epidemiology Research Unit, Departments of Obstetrics & Gynaecology and Pediatrics, Dalhousie University, Halifax, NS, Canada.
  • K S Joseph
    Department of Obstetrics & Gynaecology and School of Population & Public Health, University of British Columbia, Vancouver, BC, Canada.
  • Victoria M Allen
    Department of Obstetrics & Gynaecology, Dalhousie University, Halifax, NS, Canada.

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