Predictive performance of noninvasive factors for liver fibrosis in severe obesity: a screening based on machine learning models.

ABSTRACT:

Objectives

Liver fibrosis resulting from nonalcoholic fatty liver disease (NAFLD) and metabolic disorders is highly prevalent in patients with severe obesity and poses a significant health challenge. However, there is a lack of data on the effectiveness of noninvasive factors in predicting liver fibrosis. Therefore, this study aimed to assess the relationship between these factors and liver fibrosis through a machine learning approach.

Methods

This study involved 512 patients who underwent bariatric surgery at an outpatient clinic in Mashhad, Iran, between December 2015 and September 2021. Patients were divided into fibrosis and non-fibrosis groups and demographic, clinical, and laboratory variables were applied to develop four machine learning models: Naive Bayes (NB), logi

SUBMITTER: Jamialahmadi T 

PROVIDER: S-EPMC11741961 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature