A Predictive Model for Assistive Technology Adoption for People with Dementia
From: IEEE Xplore Digital Library - Journal of Biomedical and Health
  Informatics 

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 this 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
k-Nearest-Neighbour algorithm using seven features was found to be the
optimal classifier of assistive technology adoption for people with dementia
(prediction accuracy 0.84 +/- 0.0242).

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