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

Siew, Eu-Gene, Churilov, Leonid, Smith-Miles, Kate A. and Sturmberg, Joachim P. 2008, Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability, Lecture notes in computer science, vol. 5012, pp. 715-722.

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Title Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability
Author(s) Siew, Eu-Gene
Churilov, Leonid
Smith-Miles, Kate A.
Sturmberg, Joachim P.
Journal name Lecture notes in computer science
Volume number 5012
Start page 715
End page 722
Publisher Springer
Place of publication Berlin, Germany
Publication date 2008
ISSN 0302-9743
1611-3349
Keyword(s) doctor-patient stability (MCI)
classification and regression trees (CART)
self organising feature maps (SOFM or SOM)
supervised learning
unsupervised learning
Summary 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.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2008, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017603

Document type: Journal Article
Collection: School of Engineering and Information Technology
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