Improved Fuzzy Modeling of Thyroid Disease Detection using Interval Type 2 Fuzzy Techniques

Abstract

Fuzzy Systems are the managers for the modeling environment uncertainty for real time decision making. Type 1 fuzzy systems are much interpretable but less accurate than the type 2 and Interval Type 2 Fuzzy Systems (IT2FS). The paper introduces an experimental analysis to address the interpretability quantification and accuracy measurement in all types of fuzzy implementations. The experiment is carried out on the Thyroid dataset which leads to predict the level of Thyroid in the patients.