{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T04:05:01Z","timestamp":1784174701229,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2014Logistic Regression, Random Forest, XGBoost, LightGBM, and SVM\u2014on 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 \u00b1 0.031). Under repeated-seed cross-validation, XGBoost and LightGBM gave closely matched values (0.648 \u00b1 0.023 and 0.645 \u00b1 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 \u00b1 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\u20130.958 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.<\/jats:p>","DOI":"10.3390\/diagnostics16142197","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:05:58Z","timestamp":1784077558000},"page":"2197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6964-5339","authenticated-orcid":false,"given":"Khalid","family":"Alalawi","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering (CCSE), Taibah University, Medina 42353, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024). 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