High-Accuracy Anxiety Disorder Identification Through Subspace-Enhanced Hypergraph Neural Network

We propose a subspace-enhanced hypergraph neural network (seHGNN) for classifying anxiety disorder (AD). By leveraging a learnable incidence matrix, seHGNN strengthens the influence of hyperedges in graphs and enhances feature extraction performance of HGNNs. Then, we conduct this model within an existing binary hypothesis testing framework, where multi-modal data on the limbic system is integrated into a hypergraph. Experiments show that the seHGNN achieves a high accuracy of 90.7% for AD classification, surpassing other deep-learning-based methods, especially GNN-based methods. Our seHGNN also successfully identifies discriminative AD biomarkers, consistent with existing reports. This provides strong evidence supporting the effectiveness and interpretability of our proposed method.