Neural networks and regression analysis in the diagnosis of breast cancer

Abstract

This work is devoted to the study of the dependence of the temperature fields of the mammary glands on external conditions and the parameters of the anamnesis, and preliminary examination of patients. As a result, it was possible to significantly improve the space of thermometric diagnostic signs intended for the intelligent system. The initial set of highly informative diagnostic thermometric signs was earlier obtained by A. G. Losev and V. V. Levshinsky. To take into account the influence of external factors on the temperature during the formation of the feature space, regression models were proposed. They were built by the method of neural network modeling. These models have sufficient performance and low error value, which allows them to be used in practice. The use of neural networks made it possible to scale the database of thermometric data obtained using a combined and EMC-sensor. As a consequence, it became possible to analyze the influence of the previously revealed heterogeneity of data in the context of age and diameter of the mammary glands on the effectiveness of highly informative diagnostic signs.