A Comparative Study of ANFIS and TANFIS Hybrid Techniques for Prediction of Heart Disease

Abstract

Smart clinical decision support (SCDS) system enhances the accuracy and reduces the error in the diagnosis of a
disease. But it requires huge clinical datasets and smart algorithms for taking better decision. In last few decades many
smart hybrid algorithms have been evolved for the prediction of heart diseases. Adaptive Neuro Fuzzy Inference System
(ANFIS) and Internet of Things (IoT) based Tuned Adaptive Neuro Fuzzy Inference System (TANFIS) are two smart
hybrid algorithms have shown good accuracy in prediction. This study utilizes the Cleveland heart disease dataset for a
comparative study about the accuracy of these two methods in the prediction of heart diseases. We also evaluate the
accuracy of TANFIS as a remote SCDS system, when clinical data is fused with online data gathered through IoT.