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Title: Machine learning for improved pathological staging of prostate cancer : A performance comparison on a range of classifiers
Authors: Regnier-Coudert, Olivier
McCall, John
Lothian, Robert
Lam, Thomas
McClinton, Sam
N'Dow, James
University of Aberdeen, Natural & Computing Sciences
University of Aberdeen, School of Medicine & Dentistry, Division of Applied Health Sciences
Keywords: predictive modelling
bayesian networks
logistic regression
prostate cancer staging
partin tables
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Issue Date: May-2012
Citation: Regnier-Coudert , O , McCall , J , Lothian , R , Lam , T , McClinton , S & N'Dow , J 2012 , ' Machine learning for improved pathological staging of prostate cancer : A performance comparison on a range of classifiers ' Artificial Intelligence in Medicine , vol 55 , no. 1 , pp. 25-35 . , 10.1016/j.artmed.2011.11.003
URI: http://hdl.handle.net/2164/2417
DOI: http://dx.doi.org/10.1016/j.artmed.2011.11.003
ISSN: 0933-3657
Rights: NOTICE: this is the author’s version of a work that was accepted for publication in Artificial Intelligence in Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ARTIFICIAL INTELLIGENCE IN MEDICINE, [VOL 55, ISSUE 1, (2012)] DOI 10.1016/j.artmed.2011.11.003
Appears in Collections:Applied Health Sciences research
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