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

R.C.Y. Nuijten, U. Kaymak, P.M.E. van Gorp, M. Simons, P.E.W. van den Berg, P.M. Le Blanc

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

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 publicationProceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
EditorsVilem Novak, Vladimir Marik, Martin Stepnicka, Mirko Navara, Petr Hurtik
Pages558-565
Number of pages8
ISBN (Electronic)9789462527706
Publication statusPublished - 2020
Event11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 - Prague, Czech Republic
Duration: 9 Sep 201913 Sep 2019

Publication series

NameProceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019

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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