Cross-sectional classification of mental health risk indicators in university students using graph convolutional networks: The Atrisk-GCN framework
Year: 2026
Type: article
Abstract: Identifying students at psychological risk is a growing public-health concern in higher education. Conventional self-report questionnaires are subject to response bias and provide only static snapshots of mental health status. Graph Convolutional Networks (GCNs) offer a principled way to model feature-based relational similarities between individuals, going beyond independent-sample assumptions made by classical classifiers. In this paper we present AtRisk-GCN, a GCN-based framework for cross-sectional classification of questionnaire-derived and text-derived mental-health risk indicators in student populations. Two datasets are studied: (i) a survey-derived dataset of 1 000 records, comprising real medical students from a single institution (Faculty of Biology and Medicine, University of Lausanne, and Lausanne University Hospital, CHUV, Switzerland), drawn from the publicly available data release accompanying Carrard et al. [5]) (Data 1, continuous psychological scale scores as node features) and (ii) a publicly available mental-health text corpus of 27 977 samples (Data 2, TF-IDF representations as node features, no synthetic augmentation). In both cases, edges are constructed from cosine-similarity between node feature vectors using a k-nearest-neighbour (k-NN) scheme; no observed social-network data are available or assumed. On Data 1, AtRisk-GCN achieves an accuracy of 0.861 and an F1-score of 0.857, outperforming LSTM, CNN, and Transformer baselines. On Data 2 the model attains 0.84 accuracy and an F1-score of 0.84, surpassing, under matched input representation, TF-IDFbased Logistic Regression and Linear SVM baselines, and clearly outperforming the graph-free MLP trained on identical feature.
Institutions: Faculty of Economics and Management of Sfax, University of Sfax, Institut Supérieur du Sport et de l’Éducation Physique de Sfax, Prince Sultan University, Tunis El Manar University, École Centrale de Lyon, Université de Pau et des Pays de l'Adour, Laboratoire Informatique de l'Université de Pau et des Pays de l'Adour, Centre National de la Médecine et des Sciences des Sports
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7224339339