Using Supervised and Unsupervised Techniques to Determine Groups of Patients with Different Doctor-Patient Stability

  • Eu-Gene Siew
  • Leonid Churilov
  • Kate A. Smith-Miles
  • Joachim P. Sturmberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)


Decision trees and self organising feature maps (SOFM) are frequently used to identify groups. This research aims to compare the similarities between any groupings found between supervised (Classification and Regression Trees - CART) and unsupervised classification (SOFM), and to identify insights into factors associated with doctor-patient stability. Although CART and SOFM uses different learning paradigms to produce groupings, both methods came up with many similar groupings. Both techniques showed that self perceived health and age are important indicators of stability. In addition, this study has indicated profiles of patients that are at risk which might be interesting to general practitioners.


Doctor-patient stability (MCI) Classification and Regression Trees (CART) Self Organising Feature Maps (SOFM or SOM) supervised learning unsupervised learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Eu-Gene Siew
    • 1
  • Leonid Churilov
    • 2
  • Kate A. Smith-Miles
    • 3
  • Joachim P. Sturmberg
    • 4
  1. 1.School of Information TechnologyMonash University, Sunway Campus,Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor D.E. 
  2. 2.National Stroke Research InstituteVictoriaAustralia
  3. 3.Faculty of Science and TechnologyDeakin University, BurwoodVictoriaAustralia
  4. 4.Department of General PracticeMonash UniversityVictoriaAustralia

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