A Disease Inference Scheme based on Fuzzy Logic for Patient´s-customized Healthcare

This paper proposes a Disease Inference Scheme based on Fuzzy Logic for Patient´s-customized Healthcare. It consists of the Fuzzy-based Disease Rules Module (FDRM) and the Fuzzy-based Disease Inference Model (FDIM). The Fuzzy-based Disease Rules Module (FDRM) computes the conditional support between attributes and generates the Fuzzy Rules considering the relation between them, unlike the traditional C4.5 algorithm, by using the attributes whose conditional support is high. Therefore, because the generated Fuzzy Rules make the number of attributes decreased more than those in the traditional C4.5 algorithm, they make the accuracy of rules improved more. The Fuzzy-based Disease Inference Module (FDIM) not only can reason a patient´s disease accurately by using the generated Fuzzy Rules and a patient disease information but also can prevent a patient´s disease beforehand.