A Prediction Model Using Machine Learning Algorithm for Assessing Stone-Free Status after Single Session Shock Wave Lithotripsy to Treat Ureteral Stones.

Journal: The Journal of urology
Published Date: Dec 1, 2018

Abstract

PURPOSE: The aim of this study was to develop and validate a decision support model using a machine learning algorithm to predict treatment success after single session shock wave lithotripsy in ureteral stone cases.

Authors

  • Min Soo Choo
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea.
  • Saangyong Uhmn
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea.
  • Jong Keun Kim
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea.
  • Jun Hyun Han
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea.
  • Dong-Hoi Kim
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea.
  • Jin Kim
  • Seong Ho Lee
    Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Geonggi-do, Republic of Korea; Department of Computer Engineering, Hallym University (SU, DHK, JK), Chuncheon, Gangwon-do, Republic of Korea. Electronic address: shleeuro@hallym.ac.kr.

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