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Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases
Author(s) -
Polat Kemal,
Kara Sadιk,
Güven Ayşegül,
Güneş Salih
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2008.00501.x
Subject(s) - computer science , classifier (uml) , support vector machine , artificial intelligence , confusion matrix , decision tree , confusion , pattern recognition (psychology) , receiver operating characteristic , optic nerve , margin classifier , quadratic classifier , algorithm , machine learning , medicine , ophthalmology , psychology , psychoanalysis
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.