2020
DOI: 10.2196/16503
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Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Abstract: Background 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. Objective 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… Show more

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Cited by 61 publications
(48 citation statements)
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“…myopia and astigmatism, were also found important to predict preeclampsia by a random forest model. 25…”
Section: Discussionmentioning
confidence: 99%
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“…myopia and astigmatism, were also found important to predict preeclampsia by a random forest model. 25…”
Section: Discussionmentioning
confidence: 99%
“…For both classification and estimation tasks, machine learning algorithms have demonstrated promising performances for pregnancy outcomes 25 and other conditions. [26][27][28] Despite some hype, most of the greatest successes are diagnostic, especially deep-learning models that surpass human-level performance.…”
Section: Introductionmentioning
confidence: 99%
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“…Zejnullahu et al [ 10 ] examined the prevalence and risk factors of postpartum depression using LR. Sufriyana et al [ 11 ] used LR to explore the predictive performances for pregnancy care to inform clinicians’ decision making. Sokou et al[ 12 ] developed and validated a prediction model for clinical variables.…”
Section: Introductionmentioning
confidence: 99%
“…MLCs for normal and all glaucomatous eyes are also constructed. These machine learning algorithms have been widely applied in various healthcare and/or medical informatics applications and do not have a prior assumption about data distribution [ 35 , 36 , 37 , 38 ]. The multivariate logistic regression (LGR) was used as a benchmark for comparison.…”
Section: Methodsmentioning
confidence: 99%