Machine learning models integrating dietary data predict all-cause mortality in U.S. NAFLD patients: an NHANES-based study.

Journal: Nutrition journal
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease, closely associated with metabolic abnormalities and unhealthy lifestyle habits. Despite the critical role of diet in disease progression, most existing prognostic models for NAFLD fail to incorporate dietary factors. This study aims to integrate demographic, serological, and nutritional data. It focuses on developing machine learning models that predict all-cause mortality risk in NAFLD patients, with a particular emphasis on dietary interventions.

Authors

  • Pinchu Chen
    Division of Hepatobiliopancreatic Surgery, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Yao Li
    Center of Robotics and Intelligent Machine, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, No. 266 Fangzhen Road, Beibei District, Chongqing, 400714, China.
  • Chenfenglin Yang
    Division of Hepatobiliopancreatic Surgery, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Qifan Zhang
    Division of Hepatobiliopancreatic Surgery, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China. gdwkzqf@126.com.

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