Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study

Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study

IntroductionPreeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning algorithms demonstrate promising potential, while there is a controversial discussion...

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Bibliographic Details
Main Authors: Dongying Zheng, Xinyu Hao, Muhanmmad Khan, Lixia Wang, Fan Li, Ning Xiang, Fuli Kang, Timo Hamalainen, Fengyu Cong, Kedong Song, Chong Qiao
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
pre-eclampsia (PE)
adverse outcomes
maternal
neonatal
predictive models
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2022.959649/full

Internet

https://www.frontiersin.org/articles/10.3389/fcvm.2022.959649/full

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