Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease via Advanced Machine Learning Among Chinese Han Population.

ABSTRACT:

Background

Early and accurate diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) is crucial for implementing effective treatment and management strategies, as the disease can progress to more severe conditions such as cirrhosis and liver cancer. This study aimed to evaluate the performance of machine learning (ML) methods in detecting MASLD and provide a more effective and efficient diagnostic approach.

Methods

Data were collected from outpatient participants undergoing annual health checks at the Pudong District Health Care Service Centers in Shanghai, China. The discovery and independent validation cohorts included 8949 and 5973 participants, respectively. Initially, 47 variables were analyzed, and an ML-driven feature selection method identified

SUBMITTER: Wu N 

PROVIDER: S-EPMC12540604 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature