PUBLICATION DATE

2002-09

TITLE

Comparison of machine learning and traditional classifiers in glaucoma diagnosis.

ISSUE

9

VOLUME

49

ISSN (print)

N/A

ISSN (electronic)

N/A

ABSTRACT

Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visual-field sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visual-field test whose output is amenable to machine learning. We compared the performance of a number of machine learning algorithms with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation. The machine learning algorithms studied included multilayer perceptron (MLP), support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG). MLP and SVM are classifiers that work directly on the decision boundary and fall under the discriminative paradigm. Generative classifiers, which first model the data probability density and then perform classification via Bayes' rule, usually give deeper insight into the structure of the data space. We have applied MOG, MGG, LDA, QDA, and Parzen window to the classification of glaucoma from SAP. Performance of the various classifiers was compared by the areas under their receiver operating characteristic curves and by sensitivities (true-positive rates) at chosen specificities (true-negative rates). The machine-learning-type classifiers showed improved performance over the best indexes from STATPAC. Forward-selection and backward-elimination methodology further improved the classification rate and also has the potential to reduce testing time by diminishing the number of visual-field location measurements.

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  • Structured Analysis Of The Retina (Stare)
  • Diagnostic Innovations In Glaucoma Study: Visual Function
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    15 TRIPLES      14 PREDICATES      16 URIs      10 LITERALS

    Subject Predicate Object
    1 articles:6255734f1c88318ad5861cd992cbbfb9 sg:abstract Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visual-field sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visual-field test whose output is amenable to machine learning. We compared the performance of a number of machine learning algorithms with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation. The machine learning algorithms studied included multilayer perceptron (MLP), support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG). MLP and SVM are classifiers that work directly on the decision boundary and fall under the discriminative paradigm. Generative classifiers, which first model the data probability density and then perform classification via Bayes' rule, usually give deeper insight into the structure of the data space. We have applied MOG, MGG, LDA, QDA, and Parzen window to the classification of glaucoma from SAP. Performance of the various classifiers was compared by the areas under their receiver operating characteristic curves and by sensitivities (true-positive rates) at chosen specificities (true-negative rates). The machine-learning-type classifiers showed improved performance over the best indexes from STATPAC. Forward-selection and backward-elimination methodology further improved the classification rate and also has the potential to reduce testing time by diminishing the number of visual-field location measurements.
    2 sg:doi 10.1109/tbme.2002.802012
    3 sg:doiLink http://dx.doi.org/10.1109/tbme.2002.802012
    4 sg:isFundedPublicationOf grants:3ab295d441cc4fa373f0c42d4266a71a
    5 grants:f4a8fa1f1f66d81fc314e552ea8960f3
    6 sg:issue 9
    7 sg:language English
    8 sg:license http://scigraph.springernature.com/explorer/license/
    9 sg:publicationYear 2002
    10 sg:publicationYearMonth 2002-09
    11 sg:scigraphId 6255734f1c88318ad5861cd992cbbfb9
    12 sg:title Comparison of machine learning and traditional classifiers in glaucoma diagnosis.
    13 sg:volume 49
    14 rdf:type sg:Article
    15 rdfs:label Article: Comparison of machine learning and traditional classifiers in glaucoma diagnosis.
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