Prediction of postpartum depression in women: development and validation of multiple machine learning models.

ABSTRACT:

Background

Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several risk assessment tools for the early detection of PPD.

Methods

Candidate predictors, including history of mental illness and demographic, psychosocial, and physiological factors, were obtained from 1138 perinatal women between August 2021 and August 2022. The primary outcome of PPD was measured with the Edinburgh Postnatal Depression Scale at 6 weeks postpartum. Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared.

Results

A total of 11 potent

SUBMITTER: Qi W 

PROVIDER: S-EPMC11887113 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature