Comparing machine-learning classifiers in keratoconus diagnosis from ORA examinations

Abstract

Keratoconus identification has become a step of primary importance in the preoperative evaluation for the refractive surgery. With the ophthalmology knowledge improvement, corneal physical parameters were considered important to its evaluation. The Ocular Response Analyzer (ORA) provides some physical parameters using an applanation process to measure cornea biomechanical properties. This paper presents a study of machine learning classifiers in keratoconus diagnosis from ORA examinations. As a first use of machine learning approach with ORA parameters, this research work presents a performance comparison of the main machine learning algorithms. This approach improves ORA parameters' analysis helping ophthalmologist's efficiency in clinical diagnosis. © 2011 Springer-Verlag.