Algorithm of multidimensional analysis of main features of PCA with blurry observation of facility features detection of carcinoma cells multiple myeloma

Abstract

The article is mainly focused on the description of the PCA main component algorithm with fuzzy observation of object features. The author focuses on the application of this method to the reduction of similar traits, aiming at the diagnosis of not only the existence of cancerous rhinoceros, but also the assessment of the direction of its exposure and the degree of aggressiveness. An advantage of the developed algorithm is the ability to combine basic patient results with morphology or advanced indicators of tumor markers with microscopic or X-ray image. The occurrence is a possibility of the development of a group of people. Thanks to the fuzzy observation of the object. For this purpose, the author's points are the correlations. This gives a positive answer, where redundancy can indicate the same class. The analysis of the principal components does not exhaust the subject of research. Multidimensional exploration techniques that can improve the computerized medical diagnostics. The proposed solution can be used for unbalanced data. In the course of further research, it is possible to use the data imputation method. The method developed for selection of the traits of the subject is an original approach. It allows us to choose traits when data are either unconfirmed or incomplete. When creating statistical models for various medical institutions, the author encountered many classification problems. For doctors, the use of traditional statistical methods did not give satisfactory results. Very often the selection of traits was not very precise and could raise doubts as to its accuracy. The use of algorithms of fuzzy logic in PCA can be the foundation for a more accurate trait selection by combining different quantitative data. In the case of selecting cancer traits, it allows us to combine image data with test data. That way, it is possible to get additional information about the direction of cancer cell growth.