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Michael H. Goldbaum, Pamela A. Sample, Kwokleung Chan, Julia Williams, Te-Won Lee, Eytan Blumenthal, Christopher A. Girkin, Linda M. Zangwill, Christopher Bowd, Terrence Sejnowski, Robert N. Weinreb; Comparing Machine Learning Classifiers for Diagnosing Glaucoma from Standard Automated Perimetry. Invest. Ophthalmol. Vis. Sci. 2002;43(1):162-169. doi: https://doi.org/.
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purpose. To determine which machine learning classifier learns best to
interpret standard automated perimetry (SAP) and to compare the best of
the machine classifiers with the global indices of STATPAC 2 and with
experts in glaucoma.
methods. Multilayer perceptrons (MLP), support vector machines (SVM), mixture of
Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers
were trained and tested by cross validation on the numerical plot of
absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous
eyes, designated as such by the appearance of the optic nerve. The
authors compared performance of these classifiers with the global
indices of STATPAC, using the area under the ROC curve. Two human
experts were judged against the machine classifiers and the global
indices by plotting their sensitivity–specificity pairs.
results. MoG had the greatest area under the ROC curve of the machine
classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest
areas under the curve of the global indices. MoG had significantly
greater ROC area than PSD and CPSD. Human experts were not better at
classifying visual fields than the machine classifiers or the global
conclusions. MoG, using the entire visual field and age for input, interpreted SAP
better than the global indices of STATPAC. Machine classifiers may
augment the global indices of STATPAC.
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