A Bayesian decision analysis with fuzzy interpretability for aging chronic disease

This paper presents ideas on the applications of fuzzy concept to decision making for aging chronic disease. A Non-Homogeneous Poisson Process (NHPP) with a power-law intensity function is used in this study. In general, classical Bayesian decision methods presume that future states of nature can be characterised as probability events. However, we do not know what the future will entail probabilistically, so we devise a method to consider experts' knowledge, which are usually the absence of sharply defined criteria and to develop a fuzzy Bayesian decision process for dealing with such situations. Two cases of the discrimination problem with the aging chronic diseases are studied: (1) fuzzy states and exact information and (2) fuzzy states and fuzzy information. Finally, the fuzzy decomposition and arithmetic derivation of the experts' knowledge are presented to facilitate the development of the Bayesian decision analysis for aging chronic disease.