Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?

OBJECTIVES: To develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the ‘Random Forest’ algorithm. DESIGN: Case–control study. PARTICIPANTS: 293 eyes of...

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Detalles Bibliográficos
Autores principales: Sugimoto, Koichiro, Murata, Hiroshi, Hirasawa, Hiroyo, Aihara, Makoto, Mayama, Chihiro, Asaoka, Ryo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2013
Materias:
Ophthalmology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3796272/
https://www.ncbi.nlm.nih.gov/pubmed/24103806
http://dx.doi.org/10.1136/bmjopen-2013-003114

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3796272/
https://www.ncbi.nlm.nih.gov/pubmed/24103806
http://dx.doi.org/10.1136/bmjopen-2013-003114

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