Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

OBJECTIVES: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily a...

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Detalles Bibliográficos
Autores principales: Tsai, Cheng-Yu, Liu, Wen-Te, Hsu, Wen-Hua, Majumdar, Arnab, Stettler, Marc, Lee, Kang-Yun, Cheng, Wun-Hao, Wu, Dean, Lee, Hsin-Chien, Kuan, Yi-Chun, Wu, Cheng-Jung, Lin, Yi-Chih, Ho, Shu-Chuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989412/
https://www.ncbi.nlm.nih.gov/pubmed/36896329
http://dx.doi.org/10.1177/20552076231152751

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989412/
https://www.ncbi.nlm.nih.gov/pubmed/36896329
http://dx.doi.org/10.1177/20552076231152751

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