Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease: A fuzzy multi-criteria approach

Miguel Ortíz-Barrios; Ian Cleland; Mark Donnelly; Jonathan Greer; Antonella Petrillo; Zaury Fernández-Mendoza; Natalia Jaramillo-Rueda

doi:10.1007/978-3-030-49907-5_28

Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease: A fuzzy multi-criteria approach

Miguel Ortíz-Barrios, Ian Cleland, Mark Donnelly, Jonathan Greer, Antonella Petrillo, Zaury Fernández-Mendoza, Natalia Jaramillo-Rueda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder which requires a long-term, interdisciplinary disease management. While there remains no cure for Parkinson’s disease, treatments are available to help reduce the main symptoms and maintain quality of life for as long as possible. Owing to the global burden faced by chronic conditions such as PD, Assistive technologies (AT’s) are becoming an increasingly common prescribed form of treatment. Low adoption is hampering the potential of digital technologies within health and social care. It is then necessary to employ classification algorithms have been developed for differentiating adopters and non-adopters of these technologies; thereby, potential negative effects on people with PD and cost overruns can be further minimized. This paper bridges this gap by extending the Multi-criteria decision-making approach adopted in technology adoption modeling for people with dementia. First, the fuzzy Analytic Hierarchy Process (FAHP) is applied to estimate the initial relative weights of criteria and sub-criteria. Then, the Decision-making Trial and Evaluation Laboratory (DEMATEL) is used for evaluating the interrelations and feedback among criteria and sub-criteria. The Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS) is finally implemented to rank three classifiers (Lazy IBk – knearest neighbors, Naïve bayes, and J48 decision tree) according to their ability to model technology adoption. A real case study considering is presented to validate the proposed approach.

Original language	English
Title of host publication	Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Communication, Organization and Work - 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
Editors	Vincent G. Duffy
Publisher	Springer
Pages	390-405
Number of pages	16
Volume	12199
ISBN (Print)	9783030499068
DOIs	https://doi.org/10.1007/978-3-030-49907-5_28
Publication status	Published (in print/issue) - 10 Jul 2020
Event	11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020 - Copenhagen, Denmark Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12199 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
Country/Territory	Denmark
City	Copenhagen
Period	19/07/20 → 24/07/20

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Decision Making Trial and Evaluation Laboratory (DEMATEL)
Fuzzy Analytic Hierarchy Process (FAHP)
Healthcare
Parkinson’s disease (PD)
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
Technology adoption

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-49907-5_28

Cite this

Ortíz-Barrios, M., Cleland, I., Donnelly, M., Greer, J., Petrillo, A., Fernández-Mendoza, Z., & Jaramillo-Rueda, N. (2020). Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease: A fuzzy multi-criteria approach. In V. G. Duffy (Ed.), Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Communication, Organization and Work - 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings (Vol. 12199, pp. 390-405). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12199 LNCS). Springer. https://doi.org/10.1007/978-3-030-49907-5_28

Ortíz-Barrios, Miguel ; Cleland, Ian ; Donnelly, Mark et al. / Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease : A fuzzy multi-criteria approach. Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Communication, Organization and Work - 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. editor / Vincent G. Duffy. Vol. 12199 Springer, 2020. pp. 390-405 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Ortíz-Barrios, M, Cleland, I , Donnelly, M, Greer, J, Petrillo, A, Fernández-Mendoza, Z & Jaramillo-Rueda, N 2020, Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease: A fuzzy multi-criteria approach. in VG Duffy (ed.), Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Communication, Organization and Work - 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. vol. 12199, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12199 LNCS, Springer, pp. 390-405, 11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020, Copenhagen, Denmark, 19/07/20. https://doi.org/10.1007/978-3-030-49907-5_28

Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease: A fuzzy multi-criteria approach. / Ortíz-Barrios, Miguel; Cleland, Ian ; Donnelly, Mark et al.
Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Communication, Organization and Work - 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. ed. / Vincent G. Duffy. Vol. 12199 Springer, 2020. p. 390-405 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12199 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Ortíz-Barrios M, Cleland I , Donnelly M, Greer J, Petrillo A, Fernández-Mendoza Z et al. Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease: A fuzzy multi-criteria approach. In Duffy VG, editor, Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Communication, Organization and Work - 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. Vol. 12199. Springer. 2020. p. 390-405. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-49907-5_28

Choosing the most suitable classifier for supporting assistive technology adoption in people with Parkinson’s disease: A fuzzy multi-criteria approach

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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