The Problem of Visual Analysis of Multidimensional Medical Data

Jolita Bernatavičienė (), Gintautas Dzemyda (), Olga Kurasova (), Virginijus Marcinkevičius () and Viktor Medvedev ()
Additional contact information
Jolita Bernatavičienė: Institute of Mathematics and Informatics
Gintautas Dzemyda: Institute of Mathematics and Informatics
Olga Kurasova: Institute of Mathematics and Informatics
Virginijus Marcinkevičius: Institute of Mathematics and Informatics
Viktor Medvedev: Institute of Mathematics and Informatics

A chapter in Models and Algorithms for Global Optimization, 2007, pp 277-298 from Springer

Abstract: Summary We consider the problem of visual analysis of the multidimensional medical data. A frequent problem in medicine is an assignment of a health state to one of the known classes (for example, healthy or sick persons). A particularity of medical data classification is the fact that the transit from the normal state to diseased one is often not so conspicuous. From the table of the parametric medical multidimensional data, it is difficult to notice which objects are similar, which ones are different, i.e., which class they belong to. Therefore it is necessary to classify the multidimensional data by various classification methods. However, classification errors arc inevitable and the results of classification in medicine must be as correct as possible. That is why it is advisable to use different types of data analysis methods, for example, in addition to visualize the multidimensional data (to project to a plane). A visual analysis allows us to estimate similarities and differences of objects, a partial assignment to one or another class in simple visual way. However, the shortcoming of this analysis is the fact that while projecting multidimensional data to a plane, a part of the information is inevitably lost. Thus, one of the agreeable methods is a combination of classification and visualization methods. This synthesis lets us to obtain a more objective conclusions on the analysed data. The results, obtained by the integrated method, proposed in this chapter, can help medics to preliminary diagnose successfully or have some doubt on the former diagnosis.

Keywords: Visual Analysis; Classification Tree; Multidimensional Data; Data Mining Method; Sick Person (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-36721-7_17

Ordering information: This item can be ordered from
http://www.springer.com/9780387367217

DOI: 10.1007/978-0-387-36721-7_17

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().