Fuzzy modeling to 'understand' personal preferences of mHealth users: a case study
Raoul Nuijten, Uzay Kaymak, Pieter Van Gorp, Monique Simons, Pauline Van Den Berg, Pascale Le Blanc
Available Online August 2019.
- https://doi.org/10.2991/eusflat-19.2019.77How to use a DOI?
- fuzzy inference system Takagi-Sugeno personalization mHealth
- 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.
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 -