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)
EditorsVilem Novak, Vladimir Marik, Martin Stepnicka, Mirko Navara, Petr Hurtik
PublisherAtlantis Press
Pages558-565
Number of pages8
ISBN (Electronic)9789462527706
DOIs
Publication statusPublished - Sep 2019
Event11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 - Prague, Czech Republic
Duration: 9 Sep 201913 Sep 2019

Publication series

NameAtlantis Studies in Uncertainty Modelling
Volume1
ISSN (Print)2589-6644

Conference

Conference11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
CountryCzech Republic
CityPrague
Period9/09/1913/09/19

Keywords

  • Fuzzy inference system
  • MHealth
  • Personalization
  • Takagi-Sugeno

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