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.

Journal: Medical physics
PMID: 27147316

Abstract

PURPOSE: 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.

Authors

  • Noorazrul Yahya
    School of Physics, University of Western Australia, Western Australia 6009, Australia and School of Health Sciences, National University of Malaysia, Bangi 43600, Malaysia.
  • Martin A Ebert
    School of Physics, University of Western Australia, Western Australia 6009, Australia and Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia.
  • Max Bulsara
    Institute for Health Research, University of Notre Dame, Fremantle, Western Australia 6959, Australia.
  • Michael J House
    School of Physics, University of Western Australia, Western Australia 6009, Australia.
  • Angel Kennedy
    Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia.
  • David J Joseph
    Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia and School of Surgery, University of Western Australia, Western Australia 6009, Australia.
  • James W Denham
    School of Medicine and Public Health, University of Newcastle, New South Wales 2308, Australia.

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