Development and validation of machine learning models for MASLD: based on multiple potential screening indicators.

Journal: Frontiers in endocrinology
PMID: 39906042

Abstract

BACKGROUND: Multifaceted factors play a crucial role in the prevention and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.

Authors

  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Jingjing Zhang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Xueqin Chen
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Ling Luo
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Wenjiao Dong
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Yongjie Wang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Jiyu Zhou
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Canjin Chen
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Wenhao Wang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Wenbin Zhang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Zhiyi Zhang
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Yongguang Cai
    Department of Medical Oncology, Central Hospital of Guangdong Nongken, Zhanjiang, Guangdong, China.
  • Danli Kong
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Yuanlin Ding
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.

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