title: Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations.
abstract: OBJECTIVE
  To evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps.
  
  
  METHODS
  A total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive keratectomy (n = 11). For each map, 11 attributes were obtained or calculated from data provided by the Orbscan II. Ten-fold cross-validation was used to train and test the classifiers. Besides accuracy, sensitivity and specificity, receiver operating characteristic (ROC) curves for each classifier were generated, and the areas under the curves were calculated.
  
  
  RESULTS
  The three selected classifiers provided a good performance, and there were no differences between their performances. The area under the ROC curve of the support vector machine, multi-layer perceptron and radial basis function neural network were significantly larger than those for all individual Orbscan II attributes evaluated (p < 0.05).
  
  
  CONCLUSIONS
  Overall, the results suggest that using a support vector machine, multi-layer perceptron classifiers and radial basis function neural network, these classifiers, trained on Orbscan II data, could represent useful techniques for keratoconus detection.
authors.affiliation: Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. murilobsouza@gmail.com
authors.forename: Murilo Barreto
authors.initials: MB
authors.lastname: Souza
authors.forename: Fabricio Witzel
authors.initials: FW
authors.lastname: Medeiros
authors.forename: Danilo Barreto
authors.initials: DB
authors.lastname: Souza
authors.forename: Renato
authors.initials: R
authors.lastname: Garcia
authors.forename: Milton Ruiz
authors.initials: MR
authors.lastname: Alves
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D019781:Corneal Topography / Q000295:instrumentation*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D007640:Keratoconus / Q000145:classification* / Q000175:diagnosis*; D008297:Male; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity