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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dawber TR. The Framingham Study. The epidemiology of atherosclerotic disease. Cambridge, Harvard Univ. Press, 1980.Google Scholar
  2. 2.
    Gamberger, D., Krstacčić, G., Šmuc, T. (2000). Medical Expert Evaluation of Machine Learning Results for a Coronary Heart Disease Database. In Proc. Medical Data Analysis (ISMDA’2000), pp.159–168.Google Scholar
  3. 3.
    Gamberger, D., Krstacčić, G., Šmuc, T. (2000). Inconsistency Tests for Patient Records in a Coronary Heart Disease Database. In Proc. Medical Data Analysis (ISMDA’2000), pp.183–189.Google Scholar
  4. 4.
    Kukar, M., Grošelj, C. (1999). Machine Learning in Stepwise Diagnostic Process. In Proc. Joint European Conference in Medicine and Medical Decision Making (AIMDM’99), pp.315–325.Google Scholar
  5. 5.
    Michalski, R.S., Kaufman, K.A., (1997). Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach, Chapter 2 of: Machine Learning and Data Mining: Methods and Applications (Michalski, R.S., Bratko, I. and Kubat, M. eds), John Wiley and SonsGoogle Scholar
  6. 6.
    Witten, I.H., Eibe, F. (1999). Data Mining — Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, San Francisco.Google Scholar

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

Personalised recommendations