2018 № itm Forecasting the stage of adenomiosis with neural networks
Adenomyosis is a widespread gynecological disease, which is often accompanied by infertility. There are problems with the diagnosis of the disease, since the disease has various clinical manifestations, including often asymp- tomatic course of the disease. Due to the complexity of the diagnosis and according to different sources, its frequency varies from 5% to 70%. An equally difficult problem is determining the stage of the disease. The stage determines the tactics and strategy of treating patients. A sample of 84 patients with adenomyosis, using the Spearman rank correla- tion coefficient, revealed indicators that are interrelated with the stages of the disease. In this work, the application of the heuristic procedure to neural networks for predicting the laboratory-clinical indicators of the adenomyosis stage is considered. A software application has been developed that allows you to predict the stage of adenomyosis without resorting to hysterectomy. The methodological value of the work is that, using the example of a common gynecological disease, it is shown that the use of modern data analysis tools opens up wide possibilities for solving prognostic prob- lems of determining patients’ belonging to certain classes according to the stages or types of the disease. Software applications that automate the procedure for classifying patients can form the basis of various systems of support for making medical decisions.