title: Combining multiple HRT parameters using the 'Random Forests' method improves the diagnostic accuracy of glaucoma in emmetropic and highly myopic eyes.
abstract: OBJECTIVE
  To combine multiple Heidelberg Retina Tomograph (HRT) parameters using the Random Forests classifier to diagnose glaucoma, both in highly and physiologically myopic (highly myopic) eyes and emmetropic eyes.
  
  
  METHODS
  Subjects consisted of healthy subjects and age-matched patients with open-angle glaucoma in emmetropic (-1.0 to +1.0 diopters [D], 63 and 59 subjects, respectively) and highly myopic eyes (-10.0 to -5.0 D, 56 and 64 subjects, respectively). First, area under the receiver operating characteristic curve (AUC) was derived using 84 HRT global and sectorial parameters and the representative HRT raw parameter (largest AUC) was identified. Then, the Random Forests method was carried out using age, refractive error, and 84 HRT parameters. The AUCs were also derived using the following: (1) Frederick S. Mikelberg discriminant function (FSM) score, (2) Reinhard O.W. Burk discriminant function (RB) score, (3) Moorfields regression analysis (MRA) score, and (4) glaucoma probability score (GPS).
  
  
  RESULTS
  In combined emmetropic and highly myopic population, AUC with Random Forests method (0.96) was significantly larger than AUCs with the representative HRT raw parameter (vertical cup-to-disc ratio [global], 0.89), FSM (0.90), RB (0.83), MRA (0.87), and GPS (0.81) (P < 0.001). Similarly, AUC with the Random Forests method was significantly (P < 0.05) larger than these other parameters, both in emmetropic and highly myopic groups. Also, the Random Forests method achieved partial AUCs above 80%/90% significantly (P < 0.05) larger than any other HRT parameters in all populations.
  
  
  CONCLUSIONS
  Evaluating multiple HRT parameters using the Random Forests classifier provided accurate diagnosis of glaucoma, both in emmetropic and highly myopic eyes.
authors.affiliation: Department of Ophthalmology, University of Tokyo, Graduate School of Medicine, Tokyo, Japan.
authors.forename: Ryo
authors.initials: R
authors.lastname: Asaoka
authors.forename: Aiko
authors.initials: A
authors.lastname: Iwase
authors.forename: Tae
authors.initials: T
authors.lastname: Tsutsumi
authors.forename: Hitomi
authors.initials: H
authors.lastname: Saito
authors.forename: Shinichiro
authors.initials: S
authors.lastname: Otani
authors.forename: Kazunori
authors.initials: K
authors.lastname: Miyata
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Murata
authors.forename: Chihiro
authors.initials: C
authors.lastname: Mayama
authors.forename: Makoto
authors.initials: M
authors.lastname: Araie
mesh_terms: D000328:Adult; D000465:Algorithms; D003937:Diagnosis, Differential; D003941:Diagnostic Techniques, Ophthalmological; D058453:Emmetropia; D005260:Female; D005902:Glaucoma, Open-Angle / Q000150:complications / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009216:Myopia / Q000150:complications / Q000175:diagnosis*; D012372:ROC Curve; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology*; D012720:Severity of Illness Index; D014054:Tomography / Q000379:methods*
keywords: Heidelberg retina tomograph; Random Forests; glaucoma