An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose (sugar). Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. In this paper, we have detected on diabetes disease, which is a very common and important disease using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS. The proposed system has two stages. In the first stage, dimension of diabetes disease dataset that has 8 features is reduced to 4 features using principal component analysis. In the second stage, diagnosis of diabetes disease is conducted via adaptive neuro-fuzzy inference system classifier. We took the diabetes disease dataset used in our study from the UCI (from Department of Information and Computer Science, University of California) Machine Learning Database. The obtained classification accuracy of our system was 89.47% and it was very promising with regard to the other classification applications in literature for this problem. (c) 2006 Elsevier Inc. All rights reserved.