Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers

Journal of Glaucoma

Volumn 19, Issue 3, 2010, Pages 167-175

  Racette, Lyne ^a   Chiou, Christine Y ^a   Hao, Jiucang ^b   Bowd, Christopher ^a   Goldbaum, Michael H ^a   Zangwill, Linda M ^a   Lee, Te Won ^b   Weinreb, Robert N ^a   Sample, Pamela A ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

Glaucoma; Machine learning classifier; Neural networks; Optic disc; Structure function relationship; Visual function

Indexed keywords

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; CROSS-SECTIONAL STUDY; FEMALE; GLAUCOMA; HUMAN; INTRAOCULAR PRESSURE; LABORATORY DIAGNOSIS; MALE; MIDDLE AGED; OPTIC DISK; OPTIC NERVE DISEASE; OPTICAL COHERENCE TOMOGRAPHY; PATHOLOGY; PATHOPHYSIOLOGY; PERIMETRY; PHYSIOLOGY; PREDICTIVE VALUE; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; VISUAL DISORDER; VISUAL FIELD;

ADULT; AGED; AGED, 80 AND OVER; AREA UNDER CURVE; CROSS-SECTIONAL STUDIES; FALSE NEGATIVE REACTIONS; FEMALE; GLAUCOMA; HUMANS; INTRAOCULAR PRESSURE; MALE; MIDDLE AGED; OPTIC DISK; OPTIC NERVE DISEASES; PREDICTIVE VALUE OF TESTS; PROBABILITY; ROC CURVE; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, OPTICAL COHERENCE; VISION DISORDERS; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 79952048463     PISSN: 10570829     EISSN: 1536481X     Source Type: Journal    
DOI: 10.1097/IJG.0b013e3181a98b85     Document Type: Article

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