Informatics and Applications
2019, Volume 13, Issue 4, pp 27-29
DATA MODEL SELECTION IN MEDICAL DIAGNOSTIC TASKS
Abstract
Effective solution of medical diagnostics tasks requires the use of complex probabilistic models which
allow one to adequately describe real data and permit the use of analytical methods of the supervised learning
classification. Choosing a model of a mixture of normal distributions solves the posed problems but leads to the
curse of dimensionality. The transition to the model of a mixture of probabilistic principal component analyzers
allows one to formally set the task of choosing its structural parameters. The solution is proposed to search by
combining the application of information criteria for the formation of initial approximations followed by refinement
of the resulting estimates. Using the example of experiments to diagnose liver diseases and to predict the chemical
composition of urinary stones, the capabilities of the described data analysis procedures are demonstrated. The
proposed solutions give a source of improving the accuracy of classification, impetus to experts in the subject area
to clarify the essence of the processes.
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[+] About this article
Title
DATA MODEL SELECTION IN MEDICAL DIAGNOSTIC TASKS
Journal
Informatics and Applications
2019, Volume 13, Issue 4, pp 27-29
Cover Date
2019-12-30
DOI
10.14357/19922264190404
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
medical diagnostics; mixture of probabilistic principal component analyzers; model selection criterion; cross validation
Authors
M. P. Krivenko 
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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