{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T15:33:24Z","timestamp":1769355204105,"version":"3.49.0"},"reference-count":51,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Medical Virology"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)\u2010related liver cirrhosis (HBV\u2010LC) with poor prognoses. Early identification of HBV\u2010LC risk is essential for timely intervention. This study develops and compares nine machine learning (ML) models to predict HBV\u2010LC risk in CHB patients using routine clinical and laboratory data. A retrospective analysis was conducted involving 777 CHB patients, with 50.45% (392\/777) progressing to HBV\u2010LC. Admission data consisted of 52 clinical and laboratory variables, with missing values addressed using multiple imputation. Feature selection utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression and the Boruta algorithm, identifying 24 key variables. The evaluated ML models included XGBoost, logistic regression (LR), LightGBM, random forest (RF), AdaBoost, Gaussian naive Bayes (GNB), multilayer perceptron (MLP), support vector machine (SVM), and k\u2010nearest neighbors (KNN). The data set was partitioned into an 80% training set (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009621) and a 20% independent testing set (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009156). Cross\u2010validation (CV) facilitated hyperparameter tuning and internal validation of the optimal model. Performance metrics included the area under the receiver operating characteristic curve (AUC), Brier score, accuracy, sensitivity, specificity, and F1 score. The RF model demonstrated superior performance, with AUCs of 0.992 (training) and 0.907 (validation), while the reconstructed model achieved AUCs of 0.944 (training) and 0.945 (validation), maintaining an AUC of 0.863 in the testing set. Calibration curves confirmed a strong alignment between observed and predicted probabilities. Decision curve analysis indicated that the RF model provided the highest net benefit across threshold probabilities. The SHAP algorithm identified RPR, PLT, HBV DNA, ALT, and TBA as critical predictors. This interpretable ML model enhances early HBV\u2010LC prediction and supports clinical decision\u2010making in resource\u2010limited settings.\n                  <\/jats:p>","DOI":"10.1002\/jmv.70302","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T07:05:26Z","timestamp":1742367926000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Application of Interpretable Machine Learning Models to Predict the Risk Factors of HBV\u2010Related Liver Cirrhosis in CHB Patients Based on Routine Clinical Data: A Retrospective Cohort Study"],"prefix":"10.1002","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7198-0825","authenticated-orcid":false,"given":"Wei","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine Jingzhou Hospital Affiliated to Yangtze University Jingzhou Hubei People's Republic of China"},{"name":"Center for Scientific Research and Medical Transformation, Jingzhou Hospital Affiliated to Yangtze University  Hubei People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5273-3789","authenticated-orcid":false,"given":"Yafeng","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Jingzhou Hospital Affiliated to Yangtze University Jingzhou Hubei People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9332-3356","authenticated-orcid":false,"given":"Bing","family":"Mei","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Jingzhou Hospital Affiliated to Yangtze University Jingzhou Hubei People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Jingzhou Hospital Affiliated to Yangtze University Jingzhou Hubei People's Republic of China"},{"name":"Center for Scientific Research and Medical Transformation, Jingzhou Hospital Affiliated to Yangtze University  Hubei People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jufang","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of pediatrics Jingzhou Hospital Affiliated to Yangtze University  Hubei People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaxi","family":"Pubu","sequence":"additional","affiliation":[{"name":"Department of pediatrics Lozha County People's Hospital, Shannan, Xizang Autonomous Region  People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bu","family":"Sang","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Lozha County People's Hospital, Shannan, Xizang Autonomous Region Shannan People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0852-9190","authenticated-orcid":false,"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Jingzhou Hospital Affiliated to Yangtze University Jingzhou Hubei People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhepr.2023.100833"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/v16040618"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1159\/000456581"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.5997"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)01374-X"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.29405"},{"issue":"9","key":"e_1_2_12_8_1","article-title":"Effects of Transjugular Intrahepatic Portosystemic Shunt on Renal and Pulmonary Function in Hepatic Decompensation With and Without Hepatorenal and Hepatopulmonary Syndromes: A Review","author":"Abdelwahed A. 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