Explainable Dual‐Stream Graph Learning With Attention Fusion for Multi‐Stage Hepatitis C Classification

ABSTRACT

Hepatitis C is a chronic liver disease that can progress to fibrosis, cirrhosis and liver failure, requiring accurate multi‐stage classification. This study proposes an explainable dual‐stream deep graph neural network for five‐class classification using 615 patient records from the UCI Hepatitis C dataset. A leakage‐free pipeline employed two‐stage stratified sampling, normalization, training‐only SMOTE and sequential backward elimination, producing 350 training, 81 validation and 184 independent test samples. The framework integrates a clinical branch for biomarker representation with a KNN‐based graph branch using attention‐enhanced residual graph learning, followed by adaptive attention and multi‐scale feature fusion. The proposed model achieved 98.91% accuracy, 98.95% precision, 98.91% recall and 98.90% 1‐score, outperforming XGBoost (95.62%) and GAT (95.65%). Ablation analysis demonstrated progressive improvements from branch integration, attention fusion and multi‐scale fusion. Class‐wise ROC‐AUC ranged from 0.9854 to 1.0000. McNemar's exact test showed a statistically significant difference compared with GAT ( = 0.0312). SHAP analysis identified AST, PROT, CHE and ALB as major biomarkers with significant global and stage‐specific effects. Overall, the framework provides accurate and interpretable Hepatitis C stage classification while jointly leveraging clinical and patient‐relational information.