title: Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers.
abstract: OBJECTIVE
  To investigate whether combining optic disc topography and short-wavelength automated perimetry (SWAP) data improves the diagnostic accuracy of relevance vector machine (RVM) classifiers for detecting glaucomatous eyes compared with using each test alone.
  
  
  METHODS
  One eye of 144 glaucoma patients and 68 healthy controls from the Diagnostic Innovations in Glaucoma Study were included. RVM were trained and tested with cross-validation on optimized (backward elimination) SWAP features (thresholds plus age; pattern deviation; and total deviation) and on Heidelberg retina tomograph II (HRT) optic disc topography features, independently and in combination. RVM performance was also compared with 2 HRT linear discriminant functions and to SWAP mean deviation and pattern standard deviation. Classifier performance was measured by the area under the receiver operating characteristic curves (AUROCs) generated for each feature set and by the sensitivities at set specificities of 75%, 90%, and 96%.
  
  
  RESULTS
  RVM trained on combined HRT and SWAP thresholds plus age had significantly higher AUROC (0.93) than RVM trained on HRT (0.88) and SWAP (0.76) alone. AUROCs for the SWAP global indices (mean deviation: 0.68; pattern standard deviation: 0.72) offered no advantage over SWAP thresholds plus age, whereas the linear discriminant functions AUROCs were significantly lower than RVM trained on the combined SWAP and HRT feature set and on HRT alone feature set.
  
  
  CONCLUSIONS
  Training RVM on combined optimized HRT and SWAP data improved diagnostic accuracy compared with training on SWAP and HRT parameters alone. Future research may identify other combinations of tests and classifiers that can also improve diagnostic accuracy.
authors.affiliation: Department of Ophthalmology, Hamilton Glaucoma Center, University of California, San Diego, La Jolla, CA 92093-0946, USA. lracette@glaucoma.ucsd.edu
authors.forename: Lyne
authors.initials: L
authors.lastname: Racette
authors.forename: Christine Y
authors.initials: CY
authors.lastname: Chiou
authors.forename: Jiucang
authors.initials: J
authors.lastname: Hao
authors.forename: Christopher
authors.initials: C
authors.lastname: Bowd
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
authors.forename: Linda M
authors.initials: LM
authors.lastname: Zangwill
authors.forename: Te-Won
authors.initials: TW
authors.lastname: Lee
authors.forename: Robert N
authors.initials: RN
authors.lastname: Weinreb
authors.forename: Pamela A
authors.initials: PA
authors.lastname: Sample
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D003430:Cross-Sectional Studies; D005188:False Negative Reactions; D005260:Female; D005901:Glaucoma / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007429:Intraocular Pressure / Q000502:physiology; D008297:Male; D008875:Middle Aged; D009898:Optic Disk / Q000473:pathology*; D009901:Optic Nerve Diseases / Q000175:diagnosis* / Q000503:physiopathology; D011237:Predictive Value of Tests; D011336:Probability; D012372:ROC Curve; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence; D014786:Vision Disorders / Q000503:physiopathology*; D058609:Visual Field Tests; D014794:Visual Fields