Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.

Journal: PloS one
PMID: 31978154

Abstract

OBJECTIVE: To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and shape features obtained on dopamine transporter (DAT) single photon emission computed tomography (SPECT) are combined as a feature of machine learning (ML).

Authors

  • Takuro Shiiba
    Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, Misakimachi, Omuta-shi, Fukuoka, Japan.
  • Yuki Arimura
    Department of Radiology, Kokura Medical Center, Harugaoka, Kokura Minami-ku, Kitakyushu-shi, Fukuoka, Japan.
  • Miku Nagano
    Department of Radiology, University of Miyazaki Hospital, Kihara, Kiyotake-cho, Miyazaki-shi, Miyazaki, Japan.
  • Tenma Takahashi
    Department of Radiology, University of Miyazaki Hospital, Kihara, Kiyotake-cho, Miyazaki-shi, Miyazaki, Japan.
  • Akihiro Takaki
    Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, Misakimachi, Omuta-shi, Fukuoka, Japan.

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