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August 26, 2025Open Access

A Comprehensive Study on Postpartum Depression Prediction Using Machine Learning Approaches

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Authors

MLM.S. LekshmiSDS. Don

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Overview

Machine learning automates risk assessment for postpartum depression, suggesting enhanced screening methods for mothers.

Key Points

  • The AI-driven tool achieves a prediction accuracy of 95%, improving early identification of postpartum depression.
  • Designed using the Feed-Forward Artificial Neural Network model, it automates risk assessment based on EPDS scores.
  • Assessment includes a literature review of machine learning techniques, from traditional algorithms to deep learning approaches.
  • Highlights the potential of machine learning in transforming maternal mental health care and enhancing timely intervention.

Cite This Study

Lekshmi et al. (2025) studied this question.

synapsesocial.com/papers/68af63e3ad7bf08b1eae4487https://doi.org/10.38124/ijisrt/25jul1690
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Clinically Practical Postpartum Depression Predictor: Machine Learning Model Based on Simplified Indicators2025
  2. 2Predicting Postpartum Depression Risk Using Social Determinants of Health.2025
  3. 3Predictive Modelling of Postnatal Depression and Infant Bonding Outcomes from Self-Reported Maternal Health Data2026
  4. 4Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women2020 · 166 citations
  5. 5Development and validation of an interpretable machine learning model and online web-based calculator based on social-ecosystem theory for early prediction of postpartum depression: a longitudinal study2025 · 6 citations