Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

Yahya N 1 , Ebert MA 2 , Bulsara M 3 , House MJ 4 , Kennedy A 5 , Joseph DJ 6 Show all authors , Denham JW 7

Affiliations 

  • 1 School of Physics, University of Western Australia, Western Australia 6009, Australia and School of Health Sciences, National University of Malaysia, Bangi 43600, Malaysia
  • 2 School of Physics, University of Western Australia, Western Australia 6009, Australia and Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia
  • 3 Institute for Health Research, University of Notre Dame, Fremantle, Western Australia 6959, Australia
  • 4 School of Physics, University of Western Australia, Western Australia 6009, Australia
  • 5 Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia
  • 6 Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia and School of Surgery, University of Western Australia, Western Australia 6009, Australia
  • 7 School of Medicine and Public Health, University of Newcastle, New South Wales 2308, Australia
Med Phys, 2016 May;43(5):2040.
PMID: 27147316 DOI: 10.1118/1.4944738

Abstract

Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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