Machine learning model for HBsAg seroclearance after 48-week pegylated interferon therapy in inactive HBsAg carriers: a retrospective study.

ABSTRACT:

Purpose

To identify early predictive factors for hepatitis B surface antigen (HBsAg) clearance at week 48 following pegylated interferon (Peg-IFN) therapy in inactive HBsAg carriers (IHC), and to develop an early machine learning-based model to assist clinical decision-making.

Methods

This retrospective analysis was based on a multicenter, prospective cohort and included 777 IHC patients who received at least 48 weeks of Peg-IFN therapy. Least Absolute Shrinkage and Selection Operator (LASSO) regression and the Boruta algorithm were applied to select predictive variables. Nine machine learning models-including logistic regression (LR), decision tree (DT), and random forest (RF)-were constructed and evaluated using 10-fold cross-validation. An external validation cohort (n = 

SUBMITTER: Dong J 

PROVIDER: S-EPMC12781715 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature