A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability.

Journal: BMC medical informatics and decision making
PMID: 38017460

Abstract

BACKGROUND: Intelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification performance is often achieved by complex machine learning (ML)-based models, which causes interpretability concerns. The trade-off between accuracy and interpretability makes it challenging for most existing ML-based CTG classification models to popularize in prenatal clinical applications.

Authors

  • Junyuan Feng
    School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Jincheng Liang
    School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Zihan Qiang
    School of The Fifth Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Yuexing Hao
    Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ (Y.H., J.H., S.H.P., N.Y.Y., W.L.); Cornell University, Ithaca, NY (Y.H.); Department of Electric Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA (Y.H.); and Department of Radiation Oncology, Mayo Clinic, Rochester, MN (A.B., E.L.M., D.K.E., D.M.R., S.S., C.L.H., B.E.B., M.W.).
  • Xia Li
    Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Qinqun Chen
    School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Guiqing Liu
    First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Hang Wei
    Institute of Quality Standards & Testing Technology for Agro-products, Fujian Academy of Agricultural Sciences/ Fujian Key Laboratory of Agro-products Quality and Safety, Fuzhou, 350003, China.

Keywords

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