Using machine learning models to predict HBeAg seroconversion in CHB patients receiving pegylated interferon-α monotherapy.

ABSTRACT:

Background and objective

Though there are many advantages of pegylated interferon-α (PegIFN-α) treatment to chronic hepatitis B (CHB) patients, the response rate of PegIFN-α is only 30 ~ 40%. Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN-α.

Methods

We randomly divided 260 HBeAg-positive CHB patients who were not previously treated and received PegIFN-α monotherapy (180 μg/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 th

SUBMITTER: Shang H 

PROVIDER: S-EPMC9701889 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature