Correction of the Values of the Classification Feature of Objects on the Example of the Diagnosis of Multiple Myeloma

Abstract

The clinical features of changes in multiple myeloma indicators of different types associated with the gender of patients (objects) are considered. The methods of data mining examine the truth of the statement about the presence of many patients for whom gender is not significant in making a diagnosis. It is proposed to use the preprocessing of heterogeneous data to unify the description of objects in the binary space. The conditions for selecting and removing noise features from the set are determined. In order to reduce the dimensionality of the space, latent features are calculated by groups of binary generalized estimates of objects. A criterion is proposed for dividing patients into the optimal number of groups, taking into account their gender authenticity. From these groups, a new classification of objects is formed, differentiated by gender. The formation process is illustrated through the visualization of object descriptions, recognition accuracy and selection of informative feature sets according to the new classification. The selection procedure is implemented according to the rules of a hierarchical agglomerative algorithm. The property of invariance to the measurement scales of quantitative traits is an important argument for using the obtained results on data samples from the general population.