Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation.

Journal: Frontiers in endocrinology
PMID: 39439566

Abstract

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) represents a major global health challenge, often undiagnosed because of suboptimal screening tools. Advances in machine learning (ML) offer potential improvements in predictive diagnostics, leveraging complex clinical datasets.

Authors

  • Bo Yang
    Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.
  • Huaguan Lu
    Technology Innovation Center, Hunan University of Chinese Medicine, Changsha, China.
  • Yinghui Ran
    Department of Gastroenterology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.

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