The paper proposes the development of an approach to modelling the nature of individual morbidity based on the Big Data approach Analysis of large amounts of data requires the definition of groups of attributes that form functional dependencies However, in real datasets obtained from different sources, important relationships are defined only for a subset of attribute group values There are relationships, for example, between previously transmitted diseases and the nature of the disease now - such a relationship is established between subsets of values of different tuples and cannot be found existing methods of searching for hidden data The authors will call such dependences partial functional dependencies Accordingly, the level of support for such dependencies is low, which does not allow to use them for further data analysis At the same time, partial functional dependencies are modified associative rules, but they are executed only for a part of the data and depend on the time factor The method of finding such dependencies will be based on the modification of the method of associative rules, which allows to reduce the time complexity and to use parallel and distributed mode for calculation © 2020 Copyright for this paper by its authors
The problem of analysing the relationships between individual characteristics of individuals with COVID`19