Prediction of assistive technology adoption for people with dementia

Authors:
Shuai Zhang;Sally McClean;Chris Nugent;Sonja O'Neill;Mark Donnelly;Leo Galway;Bryan Scotney;Ian Cleland
Affiliations:
School of Computing and Information Engineering, University of Ulster, Co. Londonderry, Northern Ireland;School of Computing and Information Engineering, University of Ulster, Co. Londonderry, Northern Ireland;School of Computing and Mathematics, University of Ulster, Co. Antrim, Northern Ireland;School of Computing and Mathematics, University of Ulster, Co. Antrim, Northern Ireland;School of Computing and Mathematics, University of Ulster, Co. Antrim, Northern Ireland;School of Computing and Mathematics, University of Ulster, Co. Antrim, Northern Ireland;School of Computing and Information Engineering, University of Ulster, Co. Londonderry, Northern Ireland;School of Computing and Mathematics, University of Ulster, Co. Antrim, Northern Ireland
Venue:
HIS'13 Proceedings of the second international conference on Health Information Science
Year:
2013

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Abstract

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.