A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients.

Journal: Computational and mathematical methods in medicine
PMID: 34367315

Abstract

In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascular calcification risk in ESRD patients. As the amount of collected data is unbalanced under different risk levels, it has an influence on the classification task. So, an effective fuzzy support vector machine based on self-representation (FSVM-SR) is proposed to predict vascular calcification risk in this work. In addition, our method is also compared with other conventional machine learning methods, and the results show that our method can better complete the classification task of the vascular calcification risk.

Authors

  • Xiaobin Liu
    Department of Burns, Changhai Hospital, Second Military Medical University, Shanghai, China.
  • Xiran Zhang
    Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.
  • Xiaoyi Guo
    Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000 Wuxi, China.
  • Yijie Ding
    School of Computer Science and Technology, Tianjin University, Tianjin 300350, China. wuxi_dyj@tju.edu.cn.
  • Weiwei Shan
    Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.
  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Hua Shi
    School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China.

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