Key result
Random forest model outperforms logistic regression for predicting preeclampsia with an AUC of ~0.87.
Why the study?
Accurate screening methods for preeclampsia are a current clinical focus due to its major impact on maternal and infant outcomes.
Does a random forest model accurately predict preeclampsia in pregnant women compared to traditional models?
Population
916 pregnant women at the Second Hospital of Tianjin Medical University
Comparison
Random forest model vs logistic regression and classification tree models
Design
Retrospective single-center case-control study
Authors
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May enhance preeclampsia risk prediction; leaves open external validation before clinical adoption.
Case-Control (n=916)
No
Does a random forest model accurately predict preeclampsia in pregnant women compared to traditional models?
p-value: p=<0.05
A random forest machine learning model demonstrated superior predictive performance for preeclampsia compared to logistic regression and classification tree models.
Chen et al. (2022) conducted a case-control in Preeclampsia (n=916). Random forest (RF) prediction model vs. Logistic regression (LR) and classification tree (CT) models was evaluated on Area under the curve (AUC) for predicting preeclampsia (p=<0.05). A random forest model for predicting preeclampsia achieved an AUC of 0.871, outperforming logistic regression (AUC 0.778) and classification tree (AUC 0.850) models (P<0.05 for all comparisons).
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