Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine

To develop an applicable prediction for obstructive sleep apnea (OSA) is still a challenge in clinical practice. We apply a modern machine learning method, the support vector machine to establish a predicting model for the severity of OSA. The support vector machine was applied to build up a predict...

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Detalles Bibliográficos
Autores principales: Liu, Wen-Te, Wu, Hau-tieng, Juang, Jer-Nan, Wisniewski, Adam, Lee, Hsin-Chien, Wu, Dean, Lo, Yu-Lun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417649/
https://www.ncbi.nlm.nih.gov/pubmed/28472141
http://dx.doi.org/10.1371/journal.pone.0176991

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417649/
https://www.ncbi.nlm.nih.gov/pubmed/28472141
http://dx.doi.org/10.1371/journal.pone.0176991

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