Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

JMIR Medical Informatics
Herdiantri SufriyanaEmily Chia-Yu Su

Abstract

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making. Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for p...Continue Reading

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Citations

Aug 21, 2021·International Journal of Gynaecology and Obstetrics : the Official Organ of the International Federation of Gynaecology and Obstetrics·Yoko NagayasuYoichi Hayashi

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Methods Mentioned

BETA
cesarean section

Software Mentioned

LightGBM
BIOM
Google Scholar
MLP
Web
Clarivate Analytics
PICOTS
CHARMS
EndNote X8 (
R

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