Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mathew, Jessica Sarah; Ramasamy, Gobi Title A name given to the resource Graph Convolutional Networks for Predicting Postpartum Depression: A Symptom-Based Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETCC65847.2025.11108413" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETCC65847.2025.11108413</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015697804?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015697804?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mathew J.S., Department of Computer Science, Christ University, Bangalore, India; Ramasamy G., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource 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. Subject The topic of the resource graph convolutional networks; graph neural networks; machine learning; Postpartum depression Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152476-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online