Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)

Fuzzy modeling to 'understand' personal preferences of mHealth users: a case study

Authors
Raoul Nuijten, Uzay Kaymak, Pieter Van Gorp, Monique Simons, Pauline Van Den Berg, Pascale Le Blanc
Corresponding Author
Raoul Nuijten
Available Online August 2019.
DOI
https://doi.org/10.2991/eusflat-19.2019.77How to use a DOI?
Keywords
fuzzy inference system Takagi-Sugeno personalization mHealth
Abstract
This case study evaluates to what extent personal preferences can be automatically derived from user event data in an mHealth setting. Based on a theoretical framework, user preferences are described using six classes. Based on this framework, a structure of six Takagi-Sugeno fuzzy inference systems was constructed and evaluated against baseline data from an official survey for measuring the framework's constructs. From this analysis, it was found that user preferences may be derived from user event data using fuzzy modeling with accuracy scores that are higher than a random predictor would typically achieve.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Raoul Nuijten
AU  - Uzay Kaymak
AU  - Pieter Van Gorp
AU  - Monique Simons
AU  - Pauline Van Den Berg
AU  - Pascale Le Blanc
PY  - 2019/08
DA  - 2019/08
TI  - Fuzzy modeling to 'understand' personal preferences of mHealth users: a case study
BT  - 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.77
DO  - https://doi.org/10.2991/eusflat-19.2019.77
ID  - Nuijten2019/08
ER  -