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August 7, 2025Frontiers in MedicineOpen Access

Postpartum depression risk prediction using explainable machine learning algorithms

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Authors

XHXudong HuangThe Third Affiliated Hospital of Guangzhou University of Traditional Chinese MedicineLZLifeng ZhangShenyang Medical CollegeCZChenyang ZhangChongqing University

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Implication

Retrospective analysis predicts postpartum depression in 1,065 women, highlighting key factors using machine learning algorithms.

Key Points

  • The study developed an XGBoost model to predict postpartum depression risk in women after childbirth.
  • Among 1,065 participants, 23.5% developed postpartum depression, demonstrating significant prevalence.
  • Key predictive factors include weight gain, relationship quality, and pregnancy-related anxiety, among others.
  • The model achieved an accuracy of 95%, indicating its potential to aid healthcare professionals in identifying high-risk individuals.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/689dfe88d61984b91e13b92chttps://doi.org/10.3389/fmed.2025.1565374
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