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

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

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 pr...

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Hlavní autoři: Stefan Kuhle, Bryan Maguire, Hongqun Zhang, David Hamilton, Alexander C. Allen, K. S. Joseph, Victoria M. Allen
Médium: Článek
Jazyk:English
Vydáno: BMC 2018-08-01
Edice:BMC Pregnancy and Childbirth
Témata:
Pregnancy
Infant
Prediction
Birth weight
Fetal growth restriction
Fetal macrosomia
On-line přístup:http://link.springer.com/article/10.1186/s12884-018-1971-2

Internet

http://link.springer.com/article/10.1186/s12884-018-1971-2

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