Feature rearrangement based deep learning system for predicting heart failure mortality.

Journal: Computer methods and programs in biomedicine
Published Date: Feb 6, 2020

Abstract

BACKGROUND AND OBJECTIVE: Heart Failure is a clinical syndrome commonly caused by any structural or functional impairment. Fast and accurate mortality prediction for Heart Failure is essential to improve the health care of patients and prevent them from death. However, due to the imbalance problem and poor feature representation in Heart Failure data, mortality prediction of Heart Failure is difficult with some simple models. To handle these problems, this study is focused on proposing a fast and accurate Heart Failure mortality prediction framework.

Authors

  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Yiwen Zhu
    Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
  • Dongdong Li
    Centre for Research on Rehabilitation and Protection, Singapore.
  • Yichao Yin
    Shanghai Shuguang Hospital, Shanghai, 200025, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.

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