Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for th...

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Autors principals:	Cheng-Yu Tsai, Huei-Tyng Huang, Hsueh-Chien Cheng, Jieni Wang, Ping-Jung Duh, Wen-Hua Hsu, Marc Stettler, Yi-Chun Kuan, Yin-Tzu Lin, Chia-Rung Hsu, Kang-Yun Lee, Jiunn-Horng Kang, Dean Wu, Hsin-Chien Lee, Cheng-Jung Wu, Arnab Majumdar, Wen-Te Liu
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2022-11-01
Col·lecció:	Sensors
Matèries:	obstructive sleep apnea polysomnography anthropometric features random forest visceral fat level
Accés en línia:	https://www.mdpi.com/1424-8220/22/22/8630
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