A ubiquitous clinic recommendation system using the modified mixed-binary nonlinear programming-feedforward neural network approach

Yu Cheng Lin, Toly Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Most of the existing ubiquitous clinic recommendation (UCR) systems adopt linear mechanisms to aggregate the attribute-level performances of a clinic to evaluate the overall performance. However, such linear mechanisms may not be able to explain the choices of all patients. To solve this problem, the modified mixed binary nonlinear programming (MMBNLP)–feedforward neural network (FNN) approach is proposed in this study. In the proposed methodology, first, the existing MBNLP model is modified to improve the successful recommendation rate using a linear recommendation mechanism. Subsequently, an FNN is constructed to fit the relationship between the attribute-level performances of a clinic and its overall performance, thereby providing possible ways to further enhance the recommendation performance. The results of a regional experiment showed that the MMBNLP–FNN approach improved the successful recommendation rate by 30%.

Original languageEnglish
Pages (from-to)3282-3298
Number of pages17
JournalJournal of Theoretical and Applied Electronic Commerce Research
Volume16
Issue number7
DOIs
StatePublished - Dec 2021

Keywords

  • Clinic
  • Feedforward neural network
  • Mixed-binary nonlinear programming
  • Ubiquitous recommendation

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