Paper Info
Title
Automatic determination of diseases related to lymph system from lymphography data using principles component analysis (PCA), fuzzy weighting pre-processing and ANFIS
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.
Year
DOI
Venue
2007
10.1016/j.eswa.2006.06.004
Expert Syst. Appl.
Keywords
Field
DocType
anfis,fuzzy weighting pre-processing,classification accuracy,lymphography data,lymph diseases,lymph disease,automatic determination,principles component analysis,approach system,lymph diseases dataset,expert systems,adaptive neuro-fuzzy inference system,uci machine,fuzzy weighting method,new weighting scheme,pca,expert system,machine learning,adaptive neuro fuzzy inference system,principle component analysis
Data mining,Weighting,Computer science,Artificial intelligence,Adaptive neuro fuzzy inference system,Classifier (linguistics),Component analysis,Inference system,Pattern recognition,Expert system,Fuzzy logic,Machine learning,Lymphatic system
Journal
Volume
Issue
ISSN
33
3
Expert Systems With Applications
Citations 
PageRank 
References 
10
0.97
5
Authors
2
Name
Order
Citations
PageRank
Kemal Polat1134897.38
Salih Güneş2126778.53