TY - JOUR
T1 - A ubiquitous clinic recommendation system using the modified mixed-binary nonlinear programming-feedforward neural network approach
AU - Lin, Yu Cheng
AU - Chen, Toly
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
PY - 2021/12
Y1 - 2021/12
N2 - 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%.
AB - 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%.
KW - Clinic
KW - Feedforward neural network
KW - Mixed-binary nonlinear programming
KW - Ubiquitous recommendation
UR - http://www.scopus.com/inward/record.url?scp=85121117364&partnerID=8YFLogxK
U2 - 10.3390/jtaer16070178
DO - 10.3390/jtaer16070178
M3 - Article
AN - SCOPUS:85121117364
SN - 0718-1876
VL - 16
SP - 3282
EP - 3298
JO - Journal of Theoretical and Applied Electronic Commerce Research
JF - Journal of Theoretical and Applied Electronic Commerce Research
IS - 7
ER -