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

Kaya, Ceren; Erkaymaz, Okan; Ayar, Orhan; Özer, Mahmut

doi:10.1109/siu.2018.8404782

Cited by 2 publications

(1 citation statement)

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“…Kaya et al [3] have presented a work aimed to classify the individuals who have hypermetropia and myopia refractive disorders or not. They used horizontal and vertical Electrooculogram (EOG) signal data from the right and left eyes of the individuals.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Myopia in Adolescents through Machine Learning Methods

Guo

Qian

Wang

et al. 2020

IJERPH

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According to literature, myopia has become the second most common eye disease in China, and the incidence of myopia is increasing year by year, and showing a trend of younger age. Previous researches have shown that the occurrence of myopia is mainly determined by poor eye habits, including reading and writing posture, eye length, and so on, and parents’ heredity. In order to better prevent myopia in adolescents, this paper studies the influence of related factors on myopia incidence in adolescents based on machine learning method. A feature selection method based on both univariate correlation analysis and multivariate correlation analysis is used to better construct a feature sub-set for model training. A method based on GBRT is provided to help fill in missing items in the original data. The prediction model is built based on SVM model. Data transformation has been used to improve the prediction accuracy. Results show that our method could achieve reasonable performance and accuracy.

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Section: Related Workmentioning

confidence: 99%