Classification of refractive disorders from electrooculogram (EOG) signals by using data mining techniques

Refractive disorders are common health problems in the community and they are the most important cause of visual impairment. In this study, it was aimed to classify the individuals who have hypermetropia and myopia refractive disorders or not. For this, horizontal and vertical Electrooculogram (EOG) signal data from the right and left eyes of the individuals were used. The performance of the data was investigated by using Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF) and REP Tree (RT) data mining methods. According to the obtained results, REP Tree method has shown the most successful classification performance to detect hypermetropia and myopia refractive disorders from Electrooculogram (EOG) signals. © 2018 IEEE.

Eser Adı
[dc.title]
Classification of refractive disorders from electrooculogram (EOG) signals by using data mining techniques
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]
2018
Yayıncı
[dc.publisher]
Institute of Electrical and Electronics Engineers Inc.
Yayın Türü
[dc.type]
proceedings
Açıklama
[dc.description]
Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas
Açıklama
[dc.description]
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780
Özet
[dc.description.abstract]
Refractive disorders are common health problems in the community and they are the most important cause of visual impairment. In this study, it was aimed to classify the individuals who have hypermetropia and myopia refractive disorders or not. For this, horizontal and vertical Electrooculogram (EOG) signal data from the right and left eyes of the individuals were used. The performance of the data was investigated by using Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF) and REP Tree (RT) data mining methods. According to the obtained results, REP Tree method has shown the most successful classification performance to detect hypermetropia and myopia refractive disorders from Electrooculogram (EOG) signals. © 2018 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]
Data Mining
Konu Başlıkları
[dc.subject]
Electrooculogram (EOG)
Konu Başlıkları
[dc.subject]
Hypermetropia
Konu Başlıkları
[dc.subject]
Myopia
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Alternatif Başlık
[dc.title.alternative]
Elektrookülogram (EOG) sinyallerinden göz kırma kusurlarının veri madenciliği teknikleri kullanılarak sınıflandırılması
İlk Sayfa Sayısı
[dc.identifier.startpage]
1
Son Sayfa Sayısı
[dc.identifier.endpage]
4
Dergi Adı
[dc.relation.journal]
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/SIU.2018.8404782
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/4691
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disorders refractive myopia hypermetropia individuals performance Electrooculogram Random Forest Regression Logistic signals detect mining methods According obtained results method successful classification Refractive investigated common health problems community important visual impairment classify horizontal vertical signal
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