The Problem of Visual Analysis of Multidimensional Medical Data

  • Jolita Bernatavičienė
  • Gintautas Dzemyda
  • Olga Kurasova
  • Virginijus Marcinkevičius
  • Viktor Medvedev
Part of the Optimization and Its Applications book series (SOIA, volume 4)


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.


Visual Analysis Classification Tree Multidimensional Data Data Mining Method Sick Person 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Jolita Bernatavičienė
    • 1
  • Gintautas Dzemyda
    • 1
  • Olga Kurasova
    • 1
  • Virginijus Marcinkevičius
    • 1
  • Viktor Medvedev
    • 1
  1. 1.Institute of Mathematics and InformaticsVilniusLithuania

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