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Coronary Heart Disease Patient Models Based on Inductive Machine Learning

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

Abstract

The work presents a model construction process which is a combination of the inductive learning based detection of interesting sub- groups, comparative statistical analyses of risk factors for these groups, and expert knowledge interpretation of the results. The induced models describe population subgroups with unproportionately high rate of the disease what might be helpful in the prevention process.

Keywords

Positive Family History Coronary Heart Disease Patient Coronary Heart Disease Risk Factor Comparative Statistical Analysis Patient Model 
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

  1. 1.
    Gamberger, D. and Lavrač, N. (2000) Confirmation rule sets. In Proc. of 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000), pp.34–43.Google Scholar
  2. 2.
    Goldman, L., Garber, A.M., Grover, S.A., Hlatky, M.A. (1996) Cost-effectiveness of assessments and management of risk factors. Journal of American College Cardiology 27:1020–1030.CrossRefGoogle Scholar
  3. 3.
    Maron, D., Ridker, P.M, Pearson A.T. (1998) Risk factors and the prevention of coronary heart disease. In Wayne A.R., Schlant R.C., Fuster V.: HURST’S: The Heart, 1175–1195. McGrawc Hill, NY.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Goran Krstačič
    • 1
  • Dragan Gamberger
    • 2
  • Tomislav Šmuc
    • 2
  1. 1.Institute for Cardiovascular Prevention and RehabilitationZagrebCroatia
  2. 2.Rudjer Boškovič InstituteZagrebCroatia

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