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Prediction of Assistive Technology Adoption for People with Dementia

Zhang, Shuai, McClean, Sally I, Nugent, CD, O'Neill, Sonja, Donnelly, Mark, Galway, Leo, Scotney, Bryan and Cleland, Ian (2013) Prediction of Assistive Technology Adoption for People with Dementia. In: Health Information Science, Lecture Notes in Computer Science. Springer, London, pp. 160-171. ISBN 978-3-642-37898-0 [Book section]

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URL: http://link.springer.com/chapter/10.1007%2F978-3-642-37899-7_14

DOI: 10.1007/978-3-642-37899-7_14


Assistive technology can enhance the level of independence of people with dementia thereby increasing the possibility of remaining in their own homes. It is important that suitable technologies are selected for people with dementia, due to their reluctant to change. In our work, a predictive model has been developed for technology adoption of a Mobile Phone‐based Video Streaming solution developed for people with dementia, taking account of individual characteristics. Relevant features for technology adoption were identified and highlighted. A decision tree was then trained based on these features using Quinlan’s C4.5 algorithm. For the evaluation, repeated cross-validation was performed. Results are promising and comparable with those achieved using a logistic regression model. Statistical tests show no significant difference between the performance of a decision tree model and a logistic regression model (p=0.894). Also, the decision tree demonstrates graphically the decision making process with transparency, which is a desirable feature within healthcare based applications. In addition, the decision tree provides ease of use and interpretation and hence is easier for healthcare professionals to understand and to use both appropriately and confidently.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Smart Environments
Computer Science Research Institute > Information and Communication Engineering
ID Code:28954
Deposited By: Dr Mark Donnelly
Deposited On:28 Mar 2014 15:25
Last Modified:05 Oct 2015 10:55

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