Identifying the Most Appropriate Classifier for Underpinning Assistive Technology Adoption for People with Dementia: An Integration of Fuzzy AHP and VIKOR Methods

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12199)


Recently, the number of People with Dementia (PwD) has been rising exponentially across the world. The main symptoms that PwD experience include impairments of reasoning, memory, and thought. Owing to the burden faced by this chronic condition, Assistive Technology-based solutions (ATS) have been prescribed as a form of treatment. Nevertheless, it is widely acknowledged that low adoption rates of ATS have hampered their benefits within a health and social care context. It is then necessary to effectively discriminate between adopters and non-adopters of such solutions to avoid cost implications, improve the life quality of adopters, and find intervention alternatives for non-adopters. Several classifiers have been proposed as advancement towards the personalisation of self-management interventions for dementia in a scalable way. As multiple algorithms have been developed, an important step in technology adoption is to select the most appropriate classification alternative based on different criteria. This paper presents the integration of Fuzzy AHP (FAHP) and VIKOR to address this challenge. First, FAHP was used to calculate the criteria and sub-criteria weights under uncertainty and then VIKOR was implemented to rank the classifiers. A case study considering a mobile-based self-management and reminding solution for PwD is described to validate the proposed approach. The results revealed that Easiness of interpretation (GW = 0.192) and Handling of missing data (GW = 0.145) were the two most important criteria. Furthermore, SVM (Qj = 1.0) and AB (Qj = 0.891) were concluded to be the most suitable classifiers for supporting ATS adoption in PwD.


Technology adoption Dementia Fuzzy Analytic Hierarchy Process (FAHP) VIKOR Healthcare 



This research has received funding under the REMIND project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020, under Grant Agreement No. 734355. The authors also acknowledge the contribution of Giselle Paola Polifroni Avendaño who fully supported this investigation.


  1. 1.
    Hurst, A., Tobias, J.: Empowering individuals with do-it-yourself assistive technology. Paper Presented at the ASSETS 2011: Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 11–18 (2011)Google Scholar
  2. 2.
    Lee, C., Coughlin, J.F.: PERSPECTIVE: older adults’ adoption of technology: an integrated approach to identifying determinants and barriers. J. Prod. Innov. Manag. 32(5), 747–759 (2015)CrossRefGoogle Scholar
  3. 3.
    Kintsch, A., DePaula, R.: A framework for the adoption of assistive technology. In: SWAAAC 2002: Supporting Learning Through Assistive Technology, pp. 1–10 (2002)Google Scholar
  4. 4.
    Goodman, G., Tiene, D., Luft, P.: Adoption of assistive technology for computer access among college students with disabilities. Disabil. Rehabil. 24(1–3), 80–92 (2002)CrossRefGoogle Scholar
  5. 5.
    Pal, J., et al.: Agency in assistive technology adoption: visual impairment and smartphone use in bangalore. Paper Presented at the Conference on Human Factors in Computing Systems - Proceedings, pp. 5929–5940 (2017)Google Scholar
  6. 6.
    Cleland, I., et al.: Predicting technology adoption in people with dementia; initial results from the TAUT project. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 266–274. Springer, Cham (2014). Scholar
  7. 7.
    Chaurasia, P., et al.: Modelling assistive technology adoption for people with dementia. J. Biomed. Inform. 63, 235–248 (2016)CrossRefGoogle Scholar
  8. 8.
    Ortiz-Barrios, M., Nugent, C., Cleland, I., Donnelly, M., Verikas, A.: Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: a multicriteria framework. J. Multi-Criteria Decis. Anal. 27(1–2), 20–38 (2020)CrossRefGoogle Scholar
  9. 9.
    Zhang, S., et al.: A predictive model for assistive technology adoption for people with dementia. IEEE J. Biomed. Health Inform. 18(1), 375–383 (2014)CrossRefGoogle Scholar
  10. 10.
    Singh, S., Olugu, E.U., Musa, S.N., Mahat, A.B., Wong, K.Y.: Strategy selection for sustainable manufacturing with integrated AHP-VIKOR method under interval-valued fuzzy environment. Int. J. Adv. Manuf. Technol. 84(1–4), 547–563 (2016). Scholar
  11. 11.
    Chaghooshi, A.J., Zarchi, M.K.: Using integration of fuzzy AHP-VIKOR for selecting the best strategy in green supply chain management. Glob. J. Manag. Stud. Res. 1(1), 46–53 (2014)Google Scholar
  12. 12.
    Awasthi, A., Govindan, K., Gold, S.: Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int. J. Prod. Econ. 195, 106–117 (2018)CrossRefGoogle Scholar
  13. 13.
    Rezaie, K., Ramiyani, S.S., Nazari-Shirkouhi, S., Badizadeh, A.: Evaluating performance of Iranian cement firms using an integrated fuzzy AHP-VIKOR method. Appl. Math. Model. 38(21–22), 5033–5046 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Wang, Y., Chin, K.: Fuzzy analytic hierarchy process: a logarithmic fuzzy preference programming methodology. Int. J. Approx. Reason. 52(4), 541–553 (2011)CrossRefGoogle Scholar
  15. 15.
    Saaty, T.L.: Analytic hierarchy process. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Operations Research and Management Science, pp. 52–64. Springer, Boston (2013). Scholar
  16. 16.
    Ortiz-Barrios, M.A., Kucukaltan, B., Carvajal-Tinoco, D., Neira-Rodado, D., Jiménez, G.: Strategic hybrid approach for selecting suppliers of high-density polyethylene. J. Multi-Criteria Decis. Anal. 24(5–6), 296–316 (2017)CrossRefGoogle Scholar
  17. 17.
    Kahraman, C.: Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments, 1st edn. Springer, Boston (2008). Scholar
  18. 18.
    Kusumawardani, R., Agintiara, M.: Application of fuzzy AHP-TOPSIS method for decision making in human resource manager selection process. Procedia Comput. Sci. 72, 638–646 (2015)CrossRefGoogle Scholar
  19. 19.
    Izquierdo, N.V., et al.: Methodology of application of diffuse mathematics to performance evaluation. Int. J. Control Theory Appl. 9(44), 201–207 (2016)MathSciNetGoogle Scholar
  20. 20.
    Kułakowski, K.: Notes on order preservation and consistency in AHP. Eur. J. Oper. Res. 245(1), 333–337 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Shemshadi, A., Shirazi, H., Toreihi, M., Tarokh, M.J.: A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Syst. Appl. 38(10), 12160–12167 (2011)CrossRefGoogle Scholar
  22. 22.
    Our Yang, Y.P., Shieh, H.M., Leu, J.D., Tzeng, G.H.: A VIKOR-based multiple criteria decision method for improving information security risk. Int. J. Inf. Technol. Decis. Making 8(2), 267–287 (2009)CrossRefGoogle Scholar
  23. 23.
    Sayadi, M., Heydari, M., Shahadah, K.: Extension of VIKOR method for decision making problem with interval numbers. Appl. Math. Model. 33(5), 2257–2262 (2009)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Ortiz-Barrios, M.A., et al.: The analytic decision-making preference model to evaluate the disaster readiness in emergency departments: the ADT model. J. Multi-Criteria Decis. Anal. 24(5–6), 204–226 (2017)CrossRefGoogle Scholar
  25. 25.
    Ortiz, M.A., López-Meza, P.: Using computer simulation to improve patient flow at an outpatient internal medicine department. In: García, C.R., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds.) UCAmI 2016. LNCS, vol. 10069, pp. 294–299. Springer, Cham (2016). Scholar
  26. 26.
    Ortiz Barrios, M., Felizzola Jiménez, H., Nieto Isaza, S.: Comparative analysis between ANP and ANP-DEMATEL for six sigma project selection process in a healthcare provider. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 413–416. Springer, Cham (2014). Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Productivity and InnovationUniversidad de la Costa CUCBarranquillaColombia
  2. 2.School of Computing and MathematicsUlster UniversityJordanstownUK
  3. 3.Industrial Engineering ProgramInstitución Universitaria ITSABarranquillaColombia

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