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

Authors:
Kemal Polat;Sadık Kara;Ayşegül Güven;Salih Güneş
Affiliations:
Selcuk University, Dept. of Electrical & Electronics Engineering, 42075, Konya, Turkey;Erciyes University, Dept. of Electronics Eng., 38039, Kayseri, Turkey;Erciyes University, Civil Aviation College, Department of Electronics, 38039 Kayseri, Turkey;Selcuk University, Dept. of Electrical & Electronics Engineering, 42075, Konya, Turkey
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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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.