A Performance Comparison on the Machine Learning Classifiers in Predictive Pathology Staging of Prostate Cancer.

Journal: Studies in health technology and informatics
Published Date: Jan 1, 2017

Abstract

This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.

Authors

  • Jae Kwon Kim
    Department of Computer Science and Information Engineering, Inha University, InhaRo 100, Nam-gu, Incheon, South Korea.
  • In Hye Yook
    Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Mun Joo Choi
    Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Jong Sik Lee
    Department of Computer Science and Information Engineering, Inha University, InhaRo 100, Nam-gu, Incheon, South Korea.
  • Yong Hyun Park
    Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Ji Youl Lee
    Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • In Young Choi
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

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