Efficiency of an Algorithm Derived From Corneal Tomography Parameters to Distinguish Highly Susceptible Corneas to Ectasia From Healthy

MATHEMATICAL ALGORITHM: To build the equation extracted from SVM, 58 variables were used, some of them were extracted from the spreadsheet.. After the construction of these 58 feature vectors (FV), an SVM-derived index was created, which was called the corneal tomography multivariate index derived from a support vector machine (CTMVI). Considering that each patient represents a point on a cartesian plane with 58 dimensions (each coordinate representing one of the 58 FV), the role of SVM is to find the hyperplane that best separates the CG, KCG, and VAE-NT G subjects. A hyperplane is algebraically described by a linear equation; in this case, there are 59 coefficients, 58 of which are related to the FV and one independent coefficient representing the bias (which is a possible parallel dislocation of a given hyperplane).