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

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.

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) analysis was employed to enhance model interpretability by visualizing its decision-making process.

A total of 608 participants were followed, resulting in 384 valid questionnaires. After excluding incomplete or incorrect baseline data, 325 participants were ultimately included in the study. Among these, 130 were classified as having mild depression, and 32 were classified with major depression. Nineteen features were initially identified as being associated with PPD, with 14 retained after ENR refinement. The random forest (RF) model outperformed the other ML models. SHAP analysis identified the top five predictors of PPD: magnesium (Mg), remnant cholesterol (RC), calcium (Ca), mean corpuscular hemoglobin concentration (MCHc), and potassium (K). Mg, Ca, MCHc, and K were negatively correlated with PPD, while RC showed a positive correlation.

The RF model effectively identified associations between exposure factors and PPD. Mg, Ca, MCHc, and K were found to be protective factors, while RC emerged as a potential risk factor, highlighting its potential as a novel biomarker for PPD.