Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia.

ABSTRACT: Objectives: Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN). Methods: A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness. Results: We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and G

SUBMITTER: Liu J 

PROVIDER: S-EPMC7438882 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature