An explainable predictive machine learning model of gangrenous cholecystitis based on clinical data: a retrospective single center study.

Journal: World journal of emergency surgery : WJES
Published Date: Jan 6, 2025

Abstract

BACKGROUND: Gangrenous cholecystitis (GC) is a serious clinical condition associated with high morbidity and mortality rates. Machine learning (ML) has significant potential in addressing the diverse characteristics of real data. We aim to develop an explainable and cost-effective predictive model for GC utilizing ML and Shapley Additive explanation (SHAP) algorithm.

Authors

  • Ying Ma
    Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, Guangdong, China.
  • Man Luo
    School of Art and Design, Shanghai University of Engineering Science, Shanghai, 201620, PR. China.
  • Guoxin Guan
    Department of General Surgery, The Second Hospital of Dalian Medical University, Zhongshan Road, Shahekou District, Dalian City, Liaoning Province, 116023, China.
  • Xingming Liu
    Department of General Surgery, The Second Hospital of Dalian Medical University, Zhongshan Road, Shahekou District, Dalian City, Liaoning Province, 116023, China.
  • Xingye Cui
    Department of General Surgery, The Second Hospital of Dalian Medical University, Zhongshan Road, Shahekou District, Dalian City, Liaoning Province, 116023, China.
  • Fuwen Luo
    Department of General Surgery, The Second Hospital of Dalian Medical University, Zhongshan Road, Shahekou District, Dalian City, Liaoning Province, 116023, China. fuwenluo@aliyun.com.

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