Abstract
In this paper we study the use of medical history information extracted from the Utah Population Database (UPDB) to predict adoption of a reminder solution for people with dementia. The adoption model was built using 24 categorised features. The kNN classification algorithm gave the best performance with 85.8 % accuracy. Whilst data from the UPDB is more readily available than that in our previous work, the results highlight the benefit of including psychosocial and background information within an adoption model.
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Acknowledgements
The Alzheimer’s Association is acknowledged for supporting the TAUT project under the research grant ETAC-12-242841. Partial support for all data sets within the UPDB was provided by the University of Utah Huntsman Cancer Institute and the Huntsman Cancer Institute Cancer Center Support grant, P30 CA42014 from the National Cancer Institute.
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Chaurasia, P. et al. (2016). Impact of Medical History on Technology Adoption in Utah Population Database. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_12
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DOI: https://doi.org/10.1007/978-3-319-48799-1_12
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