Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples


KALINLI A., Sarikoc F., AKGÜN H., ÖZTÜRK F.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.110, no.3, pp.298-307, 2013 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 110 Issue: 3
  • Publication Date: 2013
  • Doi Number: 10.1016/j.cmpb.2012.12.005
  • Title of Journal : COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Page Numbers: pp.298-307

Abstract

We examined the classification and prognostic scoring performances of several computer methods on different feature sets to obtain objective and reproducible analysis of estrogen receptor status in breast cancer tissue samples.