A Ubiquitous Clinic Recommendation System Using the Modified Mixed-Binary Nonlinear Programming-Feedforward Neural Network Approach

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, th...

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Vydáno v:Journal of theoretical and applied electronic commerce research Ročník 16; číslo 7; s. 3282 - 3298
Hlavní autoři: Lin, Yu-Cheng, Chen, Toly
Médium: Journal Article
Jazyk:angličtina
Vydáno: Curicó MDPI AG 01.12.2021
Témata:
Artificial neural networks
clinic
Clinics
Coronaviruses
COVID-19
Decision making
Dentists
Electronic commerce
feedforward neural network
Methods
mixed-binary nonlinear programming
Neural networks
Nonlinear programming
Patient satisfaction
Performance evaluation
Questionnaires
Recommender systems
ubiquitous recommendation
User needs
Taiwan
ISSN:0718-1876, 0718-1876
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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%.
AbstractList 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%.
Author Lin, Yu-Cheng
Chen, Toly
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CitedBy_id crossref_primary_10_1016_j_health_2023_100147
crossref_primary_10_1007_s00500_023_09136_2
crossref_primary_10_1177_20552076231185280
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Snippet Most of the existing ubiquitous clinic recommendation (UCR) systems adopt linear mechanisms to aggregate the attribute-level performances of a clinic to...
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StartPage 3282
SubjectTerms Artificial neural networks
clinic
Clinics
Coronaviruses
COVID-19
Decision making
Dentists
Electronic commerce
feedforward neural network
Methods
mixed-binary nonlinear programming
Neural networks
Nonlinear programming
Patient satisfaction
Performance evaluation
Questionnaires
Recommender systems
ubiquitous recommendation
User needs
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Title A Ubiquitous Clinic Recommendation System Using the Modified Mixed-Binary Nonlinear Programming-Feedforward Neural Network Approach
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