UDC 007:681.512.2
DATA MINING USING HIERARCHICAL NUMBERS
IN RETROSPECTIVE DIAGNOSIS
I. Yu. Kashirin, Dr. Sc. (Tech.), full professor, RSREU, Ryazan, Russia;
orcid.org/0000-0003-1694-7410, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
A new concept of designing data mining algorithms (Data Mining) using a knowledge representation
model in an ontological form is presented. For retrospective analysis of data dynamics in the field of medical
diagnostics, the calculation of semantic similarity of concepts and features is used using applied ICF ontology.
An algebraic system of hierarchical numbers is used to analyze the semantic proximity of features and
concepts. Software implementation is based on learning data analysis algorithms. The experiments performed
using Python v.3 (Anaconda 3) tools show the effectiveness of the proposed approach.
The aim of the work is to create a science-intensive technology for designing Data Mining algorithms
with training to solve the problems of diagnostic nature.
Key words: Daia Mining, supervised algorithms, medical diagnostics, retrospective analysis, ICF ontology,
hierarchical numbers, semantic proximity, clustering.