Interval type II fuzzy rough set rule based expert system to diagnose chronic kidney disease

Abstract

Chronic kidney disease is a worldwide public health problem with an increasing incidence and prevalence, poor outcomes, and high cost. Diagnosis of Chronic Kidney Disease has always been a challenge for physicians. This paper presents an effective method for diagnosis of Chronic Kidney Disease based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-II fuzzy classification, and system evaluation. Fuzzy Rough QuickReduct algorithm feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. Rough set theory is a very useful tool for describing and modeling vagueness in ill-defined environments. In the type-II fuzzy classification step, an “indirect approach” is used for II fuzzy system modeling by implementing the Sugeno index for determining the number of rules in the fuzzy clustering approach. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose Chronic Kidney Disease with an average accuracy of 90%.