Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers

PURPOSE: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes represe...

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Autors principals: Bowd, Christopher, Weinreb, Robert N., Balasubramanian, Madhusudhanan, Lee, Intae, Jang, Giljin, Yousefi, Siamak, Zangwill, Linda M., Medeiros, Felipe A., Girkin, Christopher A., Liebmann, Jeffrey M., Goldbaum, Michael H.
Format: Artigo
Idioma:Inglês
Publicat: Public Library of Science 2014
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Research Article
Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC3907565/
https://ncbi.nlm.nih.gov/pubmed/24497932
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0085941
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