Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.

Journal: Journal of translational medicine
PMID: 40155991

Abstract

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health concern that necessitates early screening and timely intervention to improve prognosis. The current diagnostic protocols for MASLD involve complex procedures in specialised medical centres. This study aimed to explore the feasibility of utilising machine learning models to accurately screen for MASLD in large populations based on a combination of essential demographic and clinical characteristics.

Authors

  • Gangfeng Zhu
    The First Clinical Medical College, Gannan Medical University, Ganzhou, 341000, Jiangxi Province, China.
  • Yipeng Song
    Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
  • Zenghong Lu
    Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi Province, China. 1319138779@qq.com.
  • Qiang Yi
    The First Clinical Medical College, Gannan Medical University, Ganzhou, 341000, Jiangxi Province, China.
  • Rui Xu
    Collaborative Innovation Center for Green Chemical Manufacturing and Accurate Detection, Key Laboratory of Interfacial Reaction & Sensing Analysis in Universities of Shandong, School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, PR China.
  • Yi Xie
    Department of Plastic Surgery Peninsula Health Melbourne Victoria Australia.
  • Shi Geng
    Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221004, China.
  • Na Yang
    Department of Ultrasound, Affiliated Hospital of Jilin Medical College, Jilin, China.
  • Liangjian Zheng
    The First Clinical Medical College, Gannan Medical University, Ganzhou, 341000, Jiangxi Province, China.
  • Xiaofei Feng
    Jiangxi Clinical Research Center for Cancer, Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
  • Rui Zhu
    Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China.
  • Xiangcai Wang
    Jiangxi Clinical Research Center for Cancer, Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China. wangxiangcai@csco.ac.cn.
  • Li Huang
    National Research Center for Resettlement (NRCR), Hohai University, 1 Xikang Road, Nanjing 210098, China. lily8214@hhu.edu.cn.
  • Yi Xiang
    Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Keywords

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