Classification of Refractive Disorders from Electrooculogram (EOG) Signals by Using Data Mining Techniques
Ceren Kaya, Okan Erkaymaz, Orhan Ayar, M Özer, None None
proceeding (Uluslararası, Hakemli) | Yazar(lar): Ceren Kaya, Okan Erkaymaz, Orhan Ayar, M Özer, None None | Yıl: 2018 | Dergi/Kurum: 2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | İndeks: WOS.ISTP | Anahtar Kelimeler: Electrooculogram (EOG), Data Mining, Hypermetropia, Myopia — Özet: Ref
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.