Paper Info
Title
Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases
Abstract
The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.
Year
DOI
Venue
2009
10.1111/j.1468-0394.2008.00501.x
EXPERT SYSTEMS
Keywords
Field
DocType
macular disease,optic nerve disease,pattern electroretinography,C4,5 decision tree classifier,least squares support vector machine,artificial immune recognition system,performance comparison
Optic nerve diseases,Receiver operating characteristic,Confusion matrix,Macular disease,Computer science,Artificial intelligence,Classifier (linguistics),Pattern recognition,Least squares support vector machine,Algorithm,Machine learning,Decision tree learning,Optic nerve
Journal
Volume
Issue
ISSN
26.0
1.0
0266-4720
Citations 
PageRank 
References 
3
0.40
14
Authors
4
Name
Order
Citations
PageRank
Kemal Polat1134897.38
Sadik Kara227527.39
Ayşegül Güven3869.08
Salih Güneş4126778.53