title: Comparison of machine learning and traditional classifiers in glaucoma diagnosis.
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.
authors.affiliation: University of California at San Diego, La Jolla 92093, USA. kwchansalk.edu
authors.forename: Kwokleung
authors.initials: K
authors.lastname: Chan
authors.forename: Te-Won
authors.initials: TW
authors.lastname: Lee
authors.forename: Pamela A
authors.initials: PA
authors.lastname: Sample
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
authors.forename: Robert N
authors.initials: RN
authors.lastname: Weinreb
authors.forename: Terrence J
authors.initials: TJ
authors.lastname: Sejnowski
mesh_terms: D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000145:classification / Q000379:methods* / Q000592:standards; D005188:False Negative Reactions; D005189:False Positive Reactions; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009901:Optic Nerve Diseases / Q000175:diagnosis; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity; D058609:Visual Field Tests / Q000379:methods*