Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

Journal: Fa yi xue za zhi
Published Date: Jun 25, 2022

Abstract

OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application.

Authors

  • Fei-Xiang Wang
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Rui Ji
    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China.
  • Lu-Ming Zhang
    Qidong Yingwei Information Technology Co., Ltd., Qidong 226200, Jiangsu Province, China.
  • Peng Wang
    Neuroengineering Laboratory, School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China.
  • Tai-Ang Liu
    School of Materials Science and Engineering, Shanghai University, NO99, Shangda Road, Baoshan District, Shanghai, China.
  • Lu-Jie Song
    The Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, China.
  • Mao-Wen Wang
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Zhi-Lu Zhou
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Hong-Xia Hao
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Wen-tao Xia

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