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Many different medical problems are characterized by quite large spatial dimensions, which causes the task of recognizing patterns to become troublesome. This is a well-known phenomenon called curse of dimensionality. These problems force the creation of various methods of reducing dimensionality. These methods are based on selection and extraction of features. The most commonly used method in literature, regarding the later, is the analysis of the main components of pca. The natural problem of this method is the possibility of applying it to linear space. It is a natural problem to develop the pca concept for cases of nonlinear feature spaces, optimization of feature selection for principal components and the inclusion of classes in the task of supervised learning. An important problem in the perspective of machine learning is not only a reduction of features and attributes but also separation of classes. The developed method was tested in two computer experiments using real data of multiple sclerosis in children. The discussed problem, even from the very nature of the data itself, is important because it can contribute to practical implementations in medical diagnostics. The purpose of the research is to develop a method of extracting features with the application of the stochastic gradient method in the task diagnosis of multiple sclerosis in children. This solution could contribute to the increasing quality of classification and thus may be the basis for building systems that support the medical diagnostics in recognition of multiple sclerosis in children.

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Application of the Stochastic Gradient Method in the Construction of the Main Components of PCA in the Task Diagnosis of Multiple Sclerosis in Children