Application of machine learning in constructing a diagnostic model for neonatal biliary atresia.

ABSTRACT:

Importance

Early diagnosis of biliary atresia (BA) is important for advancing the Kasai operation time and improving the BA prognosis.

Objective

To develop machine learning (ML) models for neonatal BA diagnosis using clinical characteristics and serological data.

Methods

Neonates presenting with pathological jaundice between January 1, 2013, and December 31, 2023 were enrolled. Five ML models-logistic regression (LR), random forest (RF), support vector machine classifier (SVC), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)-were trained using neonatal clinical and laboratory data. The stacking classifier (SC) algorithm was employed to select the best-performing models for constructing the ensemble learning model.

Results

This study include

SUBMITTER: Wang D 

PROVIDER: S-EPMC12715887 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature