Аннотация:
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
