Information system module for analysis viral infections data based on machine learning

Abstract

The article presents results of the development information system module for analysis viral infections data. The relevance of the problem of automating the process of analyzing large volumes of data based on the use of intelligent technologies and machine learning methods is considered. The structure of the system has been developed and described, the results of design modeling of the key functionality and capabilities of the system based on the use of the UML language are presented, the basic components and technologies for implementing software are described, allowing for modularity and dynamic expandability of the potential for conducting data analysis research. The process of creating, training and testing the created machine learning models is detailed, the results of assessing the significance of the input features of the collected data set on viral diseases and the obtained values of the error matrices are described. The profiling of the operation process of the created models was carried out, the most productive and efficient of them were determined in terms of the consumption of computing resources and overall accuracy, taking into account their generalization ability