Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

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 representing sta...

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Bibliographic Details
Main Authors: Christopher Bowd, Robert N Weinreb, Madhusudhanan Balasubramanian, Intae Lee, Giljin Jang, Siamak Yousefi, Linda M Zangwill, Felipe A Medeiros, Christopher A Girkin, Jeffrey M Liebmann, Michael H Goldbaum
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3907565?pdf=render

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http://europepmc.org/articles/PMC3907565?pdf=render

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