Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon.

ABSTRACT:

Background and aim

To identify predictive factors for rapid hepatitis B surface antigen (HBsAg) seroclearance at week 24 in inactive HBsAg carriers (IHC) receiving pegylated interferon alpha-2b (Peg-IFN) therapy, and to develop a machine learning-based model to optimize individualized treatment strategies.

Methods

This retrospective analysis was based on a multicenter, prospective cohort study involving 2882 IHC patients treated with Peg-IFN and followed for at least 24 weeks. Predictive variables for week 24 HBsAg seroclearance were selected using both LASSO regression and the Boruta algorithm. Nine machine learning models were developed, including logistic regression (LR), decision tree (DT), and random forest (RF), with performance assessed via tenfold cross-validation. E

SUBMITTER: Dong J 

PROVIDER: S-EPMC12715036 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature