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Open Access Research article

Using machine learning algorithms to guide rehabilitation planning for home care clients

Mu Zhu1, Zhanyang Zhang1, John P Hirdes23 and Paul Stolee245*

Author Affiliations

1 Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada

2 Department of Health Studies and Gerontology, University of Waterloo, Waterloo, ON, Canada

3 Homewood Research Institute, Homewood Health Centre, Guelph, ON, Canada

4 School of Optometry, University of Waterloo, Waterloo, ON, Canada

5 R.B.J. Schlegel – University of Waterloo Research Institute for Aging, Waterloo, ON, Canada

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BMC Medical Informatics and Decision Making 2007, 7:41  doi:10.1186/1472-6947-7-41

Published: 20 December 2007



Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms – Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) – to guide rehabilitation planning for home care clients.


This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP.


The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP.


Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.