Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures.

Background: Hepatitis C virus (HCV) affects approximately 50 million people worldwide and progresses silently through distinct hepatic stages, yet most machine learning staging approaches offer no clinician-facing explanation and rarely evaluate whether explanations hold across architectures. Methods: We trained five models-Logistic Regression, Random Forest, XGBoost, LightGBM, and SVM-on the UCI HCV dataset (615 patients, four classes after merging the seven-instance suspect blood-donor group into a single Donor/Control class) and applied SHAP, LIME, and DiCE counterfactuals. A cross-model Spearman agreement analysis quantified feature ranking consistency, with a leakage-controlled pipeline and five-fold stratified cross-validation applied. Results: In the main 5-fold cross-validation, LightGBM achieved the highest macro-F1 (0.684 ± 0.031). Under repeated-seed cross-validation, XGBoost and LightGBM gave closely matched values (0.648 ± 0.023 and 0.645 ± 0.026), indicating comparable robustness among the boosted-tree models, both marginally ahead of the remaining three, with Random Forest close behind. Logistic Regression reached the highest macro-F1 on the single held-out split (0.790), but its cross-validated score was markedly lower (0.598 ± 0.063), underlining how unstable single-split estimates are in a small, imbalanced cohort. SHAP consistently identified AST, GGT, CHE, and ALP across the tree-based models, with CHE emerging as the leading Cirrhosis-stage marker in the boosted models. Cross-model Spearman correlations reached 0.923-0.958 among tree-based models; SHAP-LIME overlap ranged from 1/5 to 4/5. Conclusions: The framework identifies stage-specific biochemical importance patterns consistent with known HCV disease progression. The convergent finding on CHE for Cirrhosis, supported by SHAP in the tree-based models and appearing among the top LIME features in three of the five model-specific Cirrhosis explanations, supports CHE as a candidate marker worth evaluating in advanced disease.