Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

Journal: European journal of radiology
PMID: 39893823

Abstract

OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.

Authors

  • Lishan Xiao
    Department of Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yizhe Zhao
  • Yuchen Li
    Department of Medical Oncology, Shanghai Key Laboratory of Medical Epigenetics, Fudan University Shanghai Cancer Center, Institutes of Biomedical Sciences, Fudan University, 270 Dong An Rd, Shanghai, 200032, China.
  • Mengmeng Yan
    School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 102488, China.
  • Yongming Liu
    Anhui Polytechnic University, Wuhu, China.
  • Manhua Liu
    Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: mhliu@sjtu.edu.cn.
  • Chunping Ning
    Department of Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao, China. Electronic address: 152081340@qq.com.

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