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

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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.
Original languageEnglish
Title of host publication2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PublisherAtlantis Press
Pages558-565
Number of pages8
Volume1
ISBN (Electronic)978-94-6252-770-6
DOIs
Publication statusPublished - Sep 2019
Event11th Conference of the European Society for Fuzzy Logic and Technology - Prague, Czech Republic
Duration: 9 Sep 201913 Sep 2019

Conference

Conference11th Conference of the European Society for Fuzzy Logic and Technology
CountryCzech Republic
CityPrague
Period9/09/1913/09/19

Keywords

  • fuzzy inference system
  • Takagi-Sugeno
  • personalization
  • mHealth

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    Nuijten, R., Kaymak, U., Van Gorp, P., Simons, M., van den Berg, P., & Le Blanc, P. (2019). Fuzzy modeling to ‘understand’ personal preferences of mHealth users: a case study. In 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) (Vol. 1, pp. 558-565). Atlantis Press. https://doi.org/10.2991/eusflat-19.2019.77