A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease.

ABSTRACT:

Background

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of liver-related morbidity and mortality. The diagnosis of non-alcoholic steatohepatitis (NASH) plays a crucial role in the management of NAFLD patients.

Objective

The aim of our observational study was to build a machine learning model to identify NASH in NAFLD patients.

Methods

The clinical characteristics of 259 NAFLD patients and their initial laboratory data (Cohort 1) were collected to train the model and carry out internal validation. We compared the models built by five machine learning algorithms and screened out the best models. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were used to evaluate the performance of the model. In addition, the NAFLD

SUBMITTER: Yan Y 

PROVIDER: S-EPMC11539579 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature