A Linear SVM Approach for Detection of Keratoconus based on Morpho-Geometric Analysis

Abstract

There are surfaces, indexes for refractive in terms of five and four different layer thickness over a human eye. A human's eye refractive state gets affected by any of the above parameters. By considering these parameters, the cornea would be the crucial parameter from the perspective of the refractive index. This paper intends to identify the asymmetry's structural characterization of the disease with the help of morpho-geometric parameters in keratoconus affected eyes along with a slight visual curb. This work also involves the application of patient-specific virtual model analysis using the Matlab R 2014 b application. Data are classified by Support Vector Machine by identifying the greatest hyperplane which splits all other data of one class from another class for decompensating of geometric or
using modern methods and ideas.  deformation of the curvature in corneal from a singular point defined by the apex of corneal. A  regression of SVM inherited from deterioration such difference in which we declare an epsilon range from two sides of hyperplane to develop the function of regression insensible to the error not similar to SVM for differentiation in which boundary is defined to be safer to decide future.