Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability

  • Authors:

  • Eu-Gene Siew;Leonid Churilov;Kate A. Smith-Miles;Joachim P. Sturmberg

  • Affiliations:

  • School of Information Technology, Monash University, Bandar Sunway, Selangor D.E.;National Stroke Research Institute, Heidelberg Heights, Victoria, Australia;Faculty of Science and Technology, Deakin University, Burwood, Victoria, Australia;Department of General Practice, Monash University, Victoria, Australia

  • Venue:
  • PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
  • Year:
  • 2008

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Abstract

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