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Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN\u2010\u03b1.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We randomly divided 260 HBeAg\u2010positive CHB patients who were not previously treated and received PegIFN\u2010\u03b1 monotherapy (180\u2009\u03bcg\/week) into a training dataset (70%) and testing dataset (30%). The intersect features were extracted from 50 routine laboratory variables using the recursive feature elimination method algorithm, Boruta algorithm, and Least Absolute Shrinkage and Selection Operator Regression algorithm in the training dataset. After that, based on the intersect features, eight machine learning models including Logistic Regression, k\u2010Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Na\u00efve Bayes were applied to evaluate HBeAg seroconversion in HBeAg\u2010positive CHB patients receiving PegIFN\u2010\u03b1 monotherapy in the training dataset and testing dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>XGBoost model showed the best performance, which had largest AUROC (0.900, 95% CI: 0.85\u20130.95 and 0.910, 95% CI: 0.84\u20130.98, in training dataset and testing dataset, respectively), and the best calibration curve performance to predict HBeAg seroconversion. The importance of XGBoost model indicated that treatment time contributed greatest to HBeAg seroconversion, followed by HBV DNA(log), HBeAg, HBeAb, HBcAb, ALT, triglyceride, and ALP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>XGBoost model based on common laboratory variables had good performance in predicting HBeAg seroconversion in HBeAg\u2010positive CHB patients receiving PegIFN\u2010\u03b1 monotherapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcla.24667","type":"journal-article","created":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T06:43:48Z","timestamp":1664606628000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using machine learning models to predict <scp>HBeAg<\/scp> seroconversion in <scp>CHB<\/scp> patients receiving pegylated interferon\u2010\u03b1 monotherapy"],"prefix":"10.1002","volume":"36","author":[{"given":"Hongyan","family":"Shang","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, Gene Diagnosis Research Center The First Affiliated Hospital of Fujian Medical University  Fuzhou China"},{"name":"Fujian Key Laboratory of Laboratory Medicine The First Affiliated Hospital of Fujian Medical University  Fuzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhai","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Hepatopancreatobiliary Surgery The First Affiliated Hospital of Fujian Medical University  Fuzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Guo","sequence":"additional","affiliation":[{"name":"The School of Public Health Fujian Medical University  Fuzhou 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