Paper Info
Title
A Hybrid Automated Detection System Based on Least Square Support Vector Machine Classifier and k-NN Based Weighted Pre-processing for Diagnosing of Macular Disease
Abstract
In this paper, we proposed a hybrid automated detection system based least square support vector machine (LSSVM) and k-NN based weighted pre-processing for diagnosing of macular disease from the pattern electroretinography (PERG) signals. k-NN based weighted pre-processing is pre-processing method, which is firstly proposed by us. The proposed system consists of two parts: k-NN based weighted pre-processing used to weight the PERG signals and LSSVM classifier used to distinguish between healthy eye and diseased eye (macula diseases). The performance and efficiency of proposed system was conducted using classification accuracy and 10-fold cross validation. The results confirmed that a hybrid automated detection system based on the LSSVM and k-NN based weighted pre-processing has potential in detecting macular disease. The stated results show that proposed method could point out the ability of design of a new intelligent assistance diagnosis system.
Year
DOI
Venue
2007
10.1007/978-3-540-71629-7_38
ICANNGA (2)
Keywords
Field
DocType
lssvm classifier,perg signal,macular disease,macula disease,hybrid automated detection system,square support,healthy eye,weighted pre-processing,proposed system,diseased eye,vector machine classifier,least squares support vector machine,cross validation
Least squares,Data mining,Pattern recognition,Support vector machine classifier,Macular disease,Computer science,Support vector machine,Artificial intelligence,Classifier (linguistics),Cross-validation,Pattern electroretinography,Machine learning
Conference
Volume
ISSN
Citations 
4432
0302-9743
2
PageRank 
References 
Authors
0.50
4
4
Name
Order
Citations
PageRank
Kemal Polat1134897.38
Sadik Kara227527.39
Ayşegül Güven3869.08
Salih Güneş4126778.53