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Combining Unsupervised and Supervised Machine Learning in Analysis of the CHD Patient Database

  • Tomislav Šmuc
  • Dragan Gamberger
  • Goran Krstačić
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

The aim of this work is twofold: to illustrate power of unsupervised data analysis approach on routinely collected diagnostic data for coronary heart disease patients and to validate findings against cardiologist’s own patient classification and expert analysis. In this respect emphasis in this work is not on prediction and accuracy but rather on discovering paths to extraction of new insights and/or knowledge of the domain. The work demonstrates the use of unsupervised classification for the partitioning of the database with the aim of amplifying predictability of models describing expert classification, as well as boosting cause-and-effect relationships hidden in data.

Keywords

Coronary Heart Disease Patient Decision Tree Model Supervise Machine Learn Unsupervised Learning Algorithm Practical Machine Learn Tool 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Tomislav Šmuc
    • 1
  • Dragan Gamberger
    • 1
  • Goran Krstačić
    • 2
  1. 1.LIS - Rudjer Bošković InstituteZagrevCroatia
  2. 2.Institute for Cardiovascular Prevention and RehabilitationZagrebCroatia

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