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.
| Dergi Adı | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
| Sayfalar | 1 - 4 |
| Yayın Yılı | 2018 |
| 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 |
Tam Metin
Görüntülenme
Şehir
Görüntülenme
Ülke
İndirme
Şehir
İndirme
Ülke
Görüntülenme & İndirme
Görüntülenme
37
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
02 Mayıs 2023 11:30
Google Kontrol
Tıklayınız
Altmetric
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
