Laboratory variables-based artificial neural network models for predicting fatty liver disease: A retrospective study.

ABSTRACT:

Background

The efficacy of artificial neural network (ANN) models employing laboratory variables for predicting fatty liver disease (FLD) remains inadequately established. The study aimed to develop ANN models to precisely predict FLD.

Methods

Of 12,058 participants undergoing the initial FLD screening, 7,990 eligible participants were included. A total of 6,309 participants were divided randomly into the training (4,415 participants, 70%) and validation (1,894 participants, 30%) sets for developing prediction models. The performance of ANNs was additionally tested in the testing set (1,681 participants). The area under the receiver operating characteristic curve (AUROC) was employed to assess the models' performance.

Results

The 18-variable, 11-variable, 3-variable,

SUBMITTER: Lv P 

PROVIDER: S-EPMC11406433 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature