Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4.5 decision tree

'Diabetes Mellitus (Diabetes)' is a disease based on insulin hormone disorders secreted from the pancreas gland. Clinical findings find out that diabetes causes some diseases in vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases based on diabetes, and it is the leading cause of visual loss resulting from structural changes in the retinal vessels. Recent researches show that signals from vital organs can be used to diagnose diseases in the literature. In this study, the features of horizontal and vertical Video-Oculography (VOG) signals from right and left eye are used to classify non-proliferative and proliferative diabetic retinopathy disease. 25 statistical features are obtained using discrete wavelet transform with VOG signals from 24 subjects. Feature selection is performed using C4.5 decision tree algorithm from 25 features obtained. The statistical features obtained from C4.5 decision tree and discrete wavelet transform are applied as input to artificial neural networks and the classification performance of the 'Diabetic Retinopathy' disease are compared according to these two methods. Our results show that feature selection by C4.5 decision tree algorithm (96.87%) provides better classification performance than feature extraction with discrete wavelet transform (93.75%). © 2017 IEEE.

Eser Adı
[dc.title]
Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4.5 decision tree
Yazar
[dc.contributor.author]
Kaya, Ceren
Yazar
[dc.contributor.author]
Erkaymaz, Okan
Yazar
[dc.contributor.author]
Ayar, Orhan
Yazar
[dc.contributor.author]
Özer, Mahmut
Yayın Yılı
[dc.date.issued]
2017
Yayıncı
[dc.publisher]
Institute of Electrical and Electronics Engineers Inc.
Yayın Türü
[dc.type]
proceedings
Açıklama
[dc.description]
2017 Medical Technologies National Conference, TIPTEKNO 2017 -- 12 October 2017 through 14 October 2017 -- -- 134046
Özet
[dc.description.abstract]
'Diabetes Mellitus (Diabetes)' is a disease based on insulin hormone disorders secreted from the pancreas gland. Clinical findings find out that diabetes causes some diseases in vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases based on diabetes, and it is the leading cause of visual loss resulting from structural changes in the retinal vessels. Recent researches show that signals from vital organs can be used to diagnose diseases in the literature. In this study, the features of horizontal and vertical Video-Oculography (VOG) signals from right and left eye are used to classify non-proliferative and proliferative diabetic retinopathy disease. 25 statistical features are obtained using discrete wavelet transform with VOG signals from 24 subjects. Feature selection is performed using C4.5 decision tree algorithm from 25 features obtained. The statistical features obtained from C4.5 decision tree and discrete wavelet transform are applied as input to artificial neural networks and the classification performance of the 'Diabetic Retinopathy' disease are compared according to these two methods. Our results show that feature selection by C4.5 decision tree algorithm (96.87%) provides better classification performance than feature extraction with discrete wavelet transform (93.75%). © 2017 IEEE.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
tur
Konu Başlıkları
[dc.subject]
Artificial Neural Networks
Konu Başlıkları
[dc.subject]
C4.5 Decision Tree
Konu Başlıkları
[dc.subject]
Diabetic Retinopathy.
Konu Başlıkları
[dc.subject]
Discrete Wavelet Transform
Konu Başlıkları
[dc.subject]
Feature Extraction
Konu Başlıkları
[dc.subject]
Feature Selection
Konu Başlıkları
[dc.subject]
Video-Oculography (VOG)
Künye
[dc.identifier.citation]
Kaya, C., Erkaymaz, O., Ayar, O. ve Özer, M. (2017). Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4.5 decision tree. 2017 Medical Technologies National Congress (TIPTEKNO) içinde (ss. 1–4). doi:10.1109/TIPTEKNO.2017.8238093
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Alternatif Başlık
[dc.title.alternative]
C4.5 karar ağacı temelli öznitelik seçimi ile Video-Okölografi (VOG) sinyallerinden diyabetik retinopati hastalığının sınıflandırılması
İlk Sayfa Sayısı
[dc.identifier.startpage]
1
Son Sayfa Sayısı
[dc.identifier.endpage]
4
Dergi Adı
[dc.relation.journal]
2017 Medical Technologies National Conference, TIPTEKNO 2017
Dergi Cilt Bilgisi
[dc.identifier.volume]
2017-January
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/TIPTEKNO.2017.8238093
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/4687
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features discrete disease diseases signals decision obtained transform wavelet selection algorithm classification performance statistical feature Retinopathy Diabetic organs diabetes subjects better Feature provides extraction performed methods according networks neural artificial applied results compared horizontal retinopathy
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