Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis.

Journal: PloS one
Published Date: Sep 9, 2019

Abstract

AIMS: Non-linear models by machine learning may identify different risk factors with different weighting in comparison to conventional linear models.

Authors

  • Shinya Suzuki
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Takeshi Yamashita
    The Cardiovascular Institute Tokyo Japan.
  • Tsuyoshi Sakama
    Sigmaxyz, Inc, Tokyo, Japan.
  • Takuto Arita
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Naoharu Yagi
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Takayuki Otsuka
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Hiroaki Semba
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Hiroto Kano
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Shunsuke Matsuno
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Yuko Kato
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Tokuhisa Uejima
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Yuji Oikawa
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
  • Minoru Matsuhama
    Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan.
  • Junji Yajima
    Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.

Keywords

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