Multi-Label Syndrome Classification with Graph Attention Networks

In traditional Chinese medicine (TCM), accurately identifying interactions between syndromes and syndrome elements is crucial for precise diagnosis and treatment. To address this challenge, we introduce the Multi-Label Syndrome Classification model based on Graph Attention Networks (ML-SCGAN). The model uses BERT for feature extraction and GAT to capture complex interactions between symptoms and syndrome elements. Experimental results show that ML-SCGAN significantly improves classification, achieving a Micro F1 of 0.631 and a Hamming Loss of 0.033. Compared to baseline models including Binary Relevance, Classifier Chain, One-vs-rest, Label Powerset, HSVM, ML-KNN, CNN, Bi-LSTM, ML-SCGAN shows a marked improvement, outperforming the optimal baseline model HSVM by 12.3% on Micro F1. This approach enhances the efficiency of syndrome classification, promoting the intelligent application of TCM diagnosis.