Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

• Published in: Expert systems Vol. 26; no. 1; pp. 22 - 34
• Main Authors: Polat, Kemal, Kara, Sadιk, Güven, Ayşegül, Güneş, Salih
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.02.2009
• Subjects: Algorithms; artificial immune recognition system; C4.5 decision tree classifier; Classification; Decision trees; Expert systems; Eye diseases; least squares support vector machine; macular disease; Optic nerve; optic nerve disease; pattern electroretinography; performance comparison; Studies; Support vector machines
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/j.1468-0394.2008.00501.x

: 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.