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

journal contribution
posted on 2008-01-01, 00:00 authored by E G Siew, L Churilov, K Smith-Miles, J Sturmberg
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.

History

Journal

Lecture notes in computer science

Volume

5012

Pagination

715 - 722

Publisher

Springer

Location

Berlin, Germany

ISSN

0302-9743

eISSN

1611-3349

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2008, Springer-Verlag Berlin Heidelberg

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