Ucar, M.Sen, B.Cakmak, H.B.2024-09-292024-09-292013978-605010504-9https://doi.org/10.1109/eleco.2013.6713897https://hdl.handle.net/20.500.14619/94618th International Conference on Electrical and Electronics Engineering, ELECO 2013 -- 28 November 2013 through 30 November 2013 -- Bursa -- 102644Keratoconus is an eye disease characterized by progressive thinning of cornea which is the front based transparent layer of the eye. In other words, it is a progressive distortion of corneal layer and at least getting conical shape that should be like a dome camber. The vision reduces more and more while cornea gets shape of cone which should be like a sphere normally. The aim of this study is to define a new classification method for detecting keratoconus based on statistical analysis and to realize the prediction of these classified data with intelligent systems. 301 eyes of 159 patients and 394 eyes of 265 refractive surgery candidates as the control group have been used for this study. Factor analysis, one of the multivariate statistical techniques, has been mainly used to find more meaningful, easy to understand, and independent factors amongst the others. Later, a new classification method has been defined using clustering analysis techniques on these factors and finally estimated by using artificial neural networks and support vector machines. © 2013 The Chamber of Turkish Electrical Engineers-Bursa.eninfo:eu-repo/semantics/closedAccessFactor analysisIntelligent systemsNeural networksSupport vector machinesClassification methodsClustering analysisControl groupsEstimation approachesEye diseaseMultivariate statistical techniquesRefractive surgeryTransparent layersMultivariant analysisConference Object10.1109/eleco.2013.67138972-s2.0-84894123321525N/A521