Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy.

Journal: BMC urology
Published Date: Jul 3, 2020

Abstract

BACKGROUND: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods.

Authors

  • Seung Woo Yang
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Yun Kyong Hyon
    Division of Medical Mathematics, National Institute for Mathematical Sciences, 70 Yuseong-daero 1689beon-gil, Yuseong-gu, Daejeon, Republic of Korea, 34047.
  • Hyun Seok Na
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Long Jin
  • Jae Geun Lee
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Jong Mok Park
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Ji Yong Lee
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Ju Hyun Shin
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Jae Sung Lim
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Yong Gil Na
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015.
  • Kiwan Jeon
  • Taeyoung Ha
    Division of Medical Mathematics, National Institute for Mathematical Sciences, 70 Yuseong-daero 1689beon-gil, Yuseong-gu, Daejeon, Republic of Korea, 34047.
  • Jinbum Kim
    Department of Urology, Konyang University College of Medicine, Konyang University Hospital, 158 Gwanjeodong-ro, Seo-gu, Daejeon, Republic of Korea, 35365.
  • Ki Hak Song
    Department of Urology, Chungnam National University College of Medicine, Chungnam National University Hospital, 282 Monwha-ro, Jung-gu, Daejeon, Republic of Korea, 35015. urosong@cnu.ac.kr.

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