Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes.

@article{Bowd2008BayesianML,
  title={Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes.},
  author={Christopher Bowd and Jiucang Hao and Ivan M Tavares and Felipe A. Medeiros and Linda M. Zangwill and Te-Won Lee and Pamela A. Sample and Robert N. Weinreb and Michael H. Goldbaum},
  journal={Investigative ophthalmology & visual science},
  year={2008},
  volume={49 3},
  pages={945-53}
}
PURPOSE To determine whether combining structural (optical coherence tomography, OCT) and functional (standard automated perimetry, SAP) measurements as input for machine learning classifiers (MLCs; relevance vector machine, RVM; and subspace mixture of Gaussians, SSMoG) improves diagnostic accuracy for detecting glaucomatous eyes compared with using each measurement method alone. METHODS Sixty-nine eyes of 69 healthy control subjects (average age, 62.0, SD 9.7 years; visual field mean… CONTINUE READING

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Relevance vector machine for combining HRT II and SWAP results for discriminating between healthy and glaucoma eyes (abstract)

  • C Bowd, C Chiou, J Hao
  • Acta Ophthalmol Scand
  • 2006

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