Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.

ABSTRACT:

Background

Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combined with machine learning (ML) model.

Methods

This longitudinal study was conducted from June 2020 to May 2023, involving healthy pregnant women in the first trimester, followed up until the completion of the assessment in the second trimester. PPD symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS). Features with p <.05 from logistic regression were selected and refined using ENR. These features were then used to build six ML models to identify the best-performing one. SHapley Additive exPlanations (SHAP) a

SUBMITTER: Chen H 

PROVIDER: S-EPMC12060319 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature