Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort.

Journal: BMC gastroenterology
Published Date: Jul 15, 2025

Abstract

OBJECTIVES: This study aimed to develop an accurate prediction model for the risk of Non-alcoholic fatty liver disease (NAFLD) using the random survival forests (RSF), and to investigate the distribution of NAFLD risk with time.

Authors

  • Hongyu Zhang
    School of Nursing, Wenzhou Medical University, Wenzhou 325035, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Yongsheng Zhang
    Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, China.
  • Xiaowen Zhang
    Department of Foreign Languages and Literatures, Tsinghua University, Beijing, China.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.

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