Development and validation of machine learning models for MASLD: based on multiple potential screening indicators.

ABSTRACT:

Background

Multifaceted factors play a crucial role in the prevention and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.

Methods

MASLD risk prediction models were constructed based on seven machine learning algorithms using all variables, insulin-related variables, demographic characteristics variables, and other indicators, respectively. Subsequently, the partial dependence plot(PDP) method and SHapley Additive exPlanations (SHAP) were utilized to explain the roles of important variables in the model to filter out the optimal indicators for constructing the MASLD risk model.

Result

SUBMITTER: Chen H 

PROVIDER: S-EPMC11790477 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature