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.
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.