Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.

Journal: Journal of gynecologic oncology
PMID: 31074247

Abstract

OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.

Authors

  • E Sun Paik
    Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Jeong Won Lee
    Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Jeong Yeol Park
    Department of Obstetrics and Gynecology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Ju Hyun Kim
    Department of Obstetrics and Gynecology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Mijung Kim
    Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.
  • Tae Joong Kim
    Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Chel Hun Choi
    Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Byoung Gie Kim
    Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Duk Soo Bae
    Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Sung Wook Seo
    3 Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.

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