Journals of Gerontology Series A: Biological Sciences and Medical Sciences, Vol 50, Issue 6 M298-M302, Copyright © 1995 by The Gerontological Society of America
Classification trees for decision making in long-term care
M Quartararo, P Glasziou and CB Kerr
Department of Public Health, University of Sydney, Australia.
BACKGROUND. The purpose of the study was to develop a classification tool
predicting a requirement for nursing home care in a population of nursing
home applicants. In long-term care services, the objectives of
classification mechanisms will include the prevention of inappropriate
nursing home admission. METHOD. We studied 295 nursing home applicants
residing in the Lower North Shore Area of Sydney, a high socioeconomic
status area of Sydney, Australia. The predictor variables examined
included: demographic data, social work assessment data, the presence of
dementia and incontinence, the Barthel Index of Activities of Daily Living,
and the Mini-Mental State Examination. RESULTS. Classification analysis
using the C4.5 Program resulted in several classification trees for a
decision for nursing home care with sensitivities greater than 70%. The
best classification tree was one which combined the scores of the Barthel
Index and the Mini-Mental State Examination. CONCLUSION. Classification
trees in their simplicity of design and application have advantages over
other analytical methods of classification. Classification analysis and the
trees examined in this study may have future useful application in decision
making for long- term care.
