An Approach for Predicting Disease Outbreaks Using Fuzzy Inference among Physiological Variables
Eunji Lee, Chang Choi, Minchang Lee, Kunseok Oh, Pankoo Kim · 2016
The development of wireless communication technology and diverse sensors has facilitated user-centered information collection. This has also led to active research on the context-aware-based inference to provide user-tailored services. This paper proposes an approach of predicting disease outbreaks based on the fuzzy rule. There are five physiological input variables: age, blood pressure, cholesterol, obesity, and smoking. Fuzzy rule is the concept of expressing the degree of uncertain context in the membership function, which is expressed as a real number between 0 and 1. It is suitable for expression and inference of non-linear data such as physiological variables. The study performed disease outbreak risk diagnosis through the structured inference rule based on the disease outbreak element and assessed the performance of the inference system.