Graph Convolutional Networks for Predicting Postpartum Depression: A Symptom-Based Analysis

Title

Graph Convolutional Networks for Predicting Postpartum Depression: A Symptom-Based Analysis

Creator

Mathew, Jessica Sarah; Ramasamy, Gobi

Description

Postpartum Depression (PPD) is a serious mental health condition affecting new mothers and aligns with the United Nations Sustainable Development Goal (SDG) 3: Good Health and Well-being, which stresses early detection and intervention. This research investigates the use of Graph Neural Networks (GNNs)specifically, Graph Convolutional Networks (GCNs)to predict PPD by modeling the interdependencies among symptoms. A preprocessed dataset of 1,503 records was utilized, involving categorical encoding, missing value imputation, and feature standardization to enhance model reliability. The GCN model was built using a K-Nearest Neighbors (KNN)-based graph structure, enabling the network to learn intricate relationships between symptoms. Experimental results showed that the GCN achieved an accuracy of 89%, identifying key predictors such as trouble sleeping, guilt, irritability, difficulty concentrating, and anxiety. The use of SHAP explainability tools further validated these predictors, enhancing interpretability and revealing the models decision-making process. While traditional models like Random Forest achieved higher classification accuracy (95%), GCN offered valuable insights into the underlying structure and relationships among symptoms, supporting its potential in mental health diagnostics. Future work may explore hybrid architectures and larger datasets to further improve the models predictive performance and contribute to AI-driven early screening strategies for PPD. 2025 IEEE.

Source

Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

graph convolutional networks; graph neural networks; machine learning; Postpartum depression

Coverage

Mathew J.S., Department of Computer Science, Christ University, Bangalore, India; Ramasamy G., Department of Computer Science, Christ University, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152476-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ETCC65847.2025.11108413

https://www.scopus.com/pages/publications/105015697804?origin=resultslist