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| Application of BP neural network and other methods in the study of satisfaction of community health services |
| Zhuang Haishan, Bao Rui, Miao Yongqing, Wang Xinwang. |
| Department of Statistics, College of Public Health, Guangzhou Medical University, Guangzhou 510182, China |
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Abstract Objective To comprehensively evaluate the satisfaction of community health services with different algorithms, to compare various methods through case analysis, and to provide methods for the satisfaction evaluation of community health services.Methods The entropy weight method was used to calculate the objective weight of each index, and on this basis, linear summation method, gray correlation method and TOPSIS method were used to rank and compare the differences among the community health services from different dimensions of satisfaction. A high precision neural network model of satisfaction was trained and the simulation values was used to predict satisfaction evaluation the results of each dimension. Then the results were sorted and compared with traditional algorithms.Results In the traditional methods, the linear weighting sum method and the gray correlation method were used to evaluate four dimensions of 21 community health service institutions with the best S17 and the worst S2. In the TOPSIS method, the best accessible dimension was S17, and the worst was S9. The other three dimensions were S17 best and S2 worst. The correlation between the results of the three traditional methods was statistically significant, and the correlation coefficient was between 0.894 and 1.000 (P<0.001). In BP neural network, the optimal values of the four dimensions were S17 with the worst S2. The simulation errors of each dimension were 0.003, 0.002, 0.002 and 0.003. The correlation coefficient between BP neural network and the three traditional methods was between 0.891 and 1.000 (P<0.001).Conclusion All the methods are applicable to the satisfaction evaluation of community health services, and decisionmakers can choose different methods to evaluate the satisfaction of community health service institutions according to the actual situation.
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Received: 18 January 2019
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