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A predictive model for assistive technology adoption for people with dementia

Zhang, Shuai, McClean, Sally, Nugent, CD, Donnelly, Mark, Galway, Leo, Scotney, BW and Cleland, Ian (2014) A predictive model for assistive technology adoption for people with dementia. IEEE Journal of Biomedical and Health Informatics, 18 (1). pp. 375-383. [Journal article]

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URL: http://dx.doi.org/10.1109/JBHI.2013.2267549

DOI: doi:10.1109/JBHI.2013.2267549


Assistive technology has the potential to enhance the level of independence of people with dementia, thereby increasing the possibility of supporting home-based care. In general, people with dementia are reluctant to change; therefore it is important that suitable assistive technologies are selected for them. Consequently, the development of predictive models that are able to determine a person’s potential to adopt a particular technology is desirable. In the current paper, a predictive adoption model for a Mobile Phone-based Video Streaming system, developed for people with dementia, is presented. Taking into consideration characteristics related to a person’s ability, living arrangements and preferences, this paper discusses the development of predictive models, which were based on a number of carefully selected data mining algorithms for classification. For each, the learning on different relevant features for technology adoption has been tested, in conjunction with handling the imbalance of available data for output classes. Given our focus on providing predictive tools that could be used and interpreted by healthcare professionals, models with ease-of-use, intuitive understanding and clear decision making processes are preferred. Predictive models have therefore been evaluated, on a multi-criterion basis, in terms of their prediction performance, robustness, bias with regard to two types of errors and usability. Overall, the model derived from incorporating a kNN algorithm using 7 features was found to be the optimal classifier of assistive technology adoption for people with dementia (prediction accuracy 0.84±0.0242).

Item Type:Journal article
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:26993
Deposited By: Dr Mark Donnelly
Deposited On:16 Sep 2013 08:27
Last Modified:05 Oct 2015 10:54

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