Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

STUDY OBJECTIVES: Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability o...

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Detalles Bibliográficos
Autores principales: Huang, Wen-Chi, Lee, Pei-Lin, Liu, Yu-Ting, Chiang, Ambrose A, Lai, Feipei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Big Data Approaches to Sleep and Circadian Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355399/
https://www.ncbi.nlm.nih.gov/pubmed/31917446
http://dx.doi.org/10.1093/sleep/zsz295

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355399/
https://www.ncbi.nlm.nih.gov/pubmed/31917446
http://dx.doi.org/10.1093/sleep/zsz295

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