Optimization of chronic diseases surveillance points based on the fuzzy matter-element theory

Objective To explore the feasibility of applying a fuzzy matter-element model to the selection and optimization of diseases surveillance points. Methods The chronic diseases surveillance data of a region were classified and optimum items picked out based on the fuzzy matter-element theory, the results of which were verified using analysis of variance and system clustering. Results The 28 surveillance points in the region could be classified into 4 groups based on the fuzzy matter-element theory, and the 10 optimum surveillance points selected according to the surveillance data of the current year could remain as the optimum ones for the next year. Multivariate analysis of variance suggested no statistical difference between the 10 surveillance points selected based on the fuzzy matter-element model and the 28 original ones. System clustering results were basically consistent with the classification derived from the fuzzy matter-element theory. Conclusion The fuzzy matter-element model, which provided stable and representative optimization results, could be a recommended method for selecting and optimizing diseases surveillance points.