Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

Journal: BMC ophthalmology
Published Date: Jan 27, 2015

Abstract

BACKGROUND: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD).

Authors

  • Paolo Fraccaro
    Centre for Health Informatics, City University London, London, UK.
  • Massimo Nicolo
    Di.N.O.G.Mi, University of Genoa, L.go P. Daneo 3, Genoa, 16132, Italy. massimonicolo@gmail.com.
  • Monica Bonetto
    DIBRIS, University of Genoa, Genoa, Italy.
  • Mauro Giacomini
    DIBRIS, University of Genoa, Genoa, Italy.
  • Peter Weller
    Centre for Health Informatics, City University London, London, UK.
  • Carlo Enrico Traverso
    Di.N.O.G.Mi, University of Genoa, L.go P. Daneo 3, Genoa, 16132, Italy.
  • Mattia Prosperi
    University of Florida, Gainesville, Florida, USA.
  • Dympna OSullivan
    Centre for Health Informatics, City University London, London, UK.

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