Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023.

Journal: BMC gastroenterology
PMID:

Abstract

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use machine learning in the identification of significant features and the development of a predictive model to determine its usefulness in discrimination of MAFLD's risk stratification (low, moderate, and high) in adults.

Authors

  • Liqiong Huang
    Department of Ultrasound, Chengdu Integrated Traditional Chinese Medicine and Western Medicine Hospital, Sichuan Province, No. 18 Wanxiang North Road, High Tech Zone, Chengdu, China.
  • Yu Luo
    Department of Radiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Mengqi Wu
    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China. mengqi.wu@whu.edu.cn.
  • Lirong Hu
    Department of Ultrasound, Chengdu Integrated Traditional Chinese Medicine and Western Medicine Hospital, Sichuan Province, No. 18 Wanxiang North Road, High Tech Zone, Chengdu, China. 277373164@qq.com.