Abstract
The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed information on background, psychosocial, and medical history of the subjects. In the process of modelling adoption, feature selection is carried out followed by empirical analysis to identify the best classification models. With a more detailed set of features including psychosocial and medical history information, the developed adoption model, using kNN algorithm, achieved a prediction accuracy of 99.41% when tested on 173 participants. The second-best algorithm built, using NN, achieved 94.08% accuracy. Both these results have improved accuracy in comparison to the best accuracy achieved (92.48%) in our previous work, based on psychosocial and self-reported health data for the same cohort. It has been found that psychosocial data is better than medical data for predicting technology adoption. However, for the best results, we should use a combination of psychosocial and medical data where it is preferable that the latter is provided from reliable medical sources, rather than self-reported.
Highlights
The challenges of caring for an increasing number of the population above 65 are placing a huge burden on existing healthcare services [1]
The aim of this research is to carry out an early-stage evaluation of assistive technologies and analyse the reasons that affect their adoption in a cohort of older adults with cognitive impairment
In the second stage of the work, we considered data about the number of times a Technology Adoption and Usage Tool (TAUT) participant is hospitalised in the category of inpatient discharge/hospitalisations (HOSP) and ambulatory surgery (AS) from the year 1996 to 2013 for the 10 most prevalent diseases [5]
Summary
The challenges of caring for an increasing number of the population above 65 are placing a huge burden on existing healthcare services [1]. This burden is further exacerbated by the prevalence of cognitive impairment, which is a common problem observed at this age. State University, Logan, USA to a more severe types of cognitive disease such as Alzheimer’s disease (AD) In such cases, the affected patient may no longer be able to live independently at home and may require external help to carry out activities of daily living [2]. The aim of this research is to carry out an early-stage evaluation of assistive technologies and analyse the reasons that affect their adoption in a cohort of older adults with cognitive impairment
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