Development and Validation of Machine Learning-Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease.

ABSTRACT: Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease and is considered the hepatic manifestation of metabolic syndrome, triggering out adverse outcomes. A stacked multimodal machine learning model is constructed and validated for early identification and prognosis stratification of NAFLD by integrating genetic and clinical data sourced from 36 490 UK Biobank and 9 007 Nanfang Hospital participants and extracted its probabilities as in-silico scores for NAFLD (ISNLD). The efficacy of ISNLD is evaluated for the early prediction of severe liver disease (SeLD) and analyzed its association with metabolism-related outcomes. The multimodal model performs satisfactorily in classifying individuals into low- and high-risk groups for NAFLD, achieving area under curve

SUBMITTER: Xiao L 

PROVIDER: S-EPMC12412496 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature