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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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907565/
https://www.ncbi.nlm.nih.gov/pubmed/24497932
http://dx.doi.org/10.1371/journal.pone.0085941

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