Abstract
Purchase PDF (359 K)
E-mail Article
Add to my Quick Links
Cited By in Scopus (0)
Purchase PDF (265 K)
doi:10.1016/j.eswa.2006.06.004 
Copyright © 2006 Elsevier Ltd All rights reserved.
Automatic determination of diseases related to lymph system from lymphography data using principles component analysis (PCA), fuzzy weighting pre-processing and ANFIS
Available online 7 July 2006.
References and further reading may be available for this article. To view references and further reading you must purchase this article.
Abstract
It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lymph diseases, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lymph diseases using principles component analysis (PCA), fuzzy weighting pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The approach system has three stages. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to four features using principles component analysis. In the second stage, a new weighting scheme based on fuzzy weighting method was utilized as a pre-processing step before the main classifier. Then, in the third stage, ANFIS was our used classifier. We took the lymph diseases dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 88.83% and it was very promising with regard to the other classification applications in the literature for this problem.
Keywords: Lymph diseases; PCA; ANFIS; Fuzzy weighting pre-processing; Expert systems
