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

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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Bibliographic Details
Main Authors: 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: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Sensors
Subjects:
obstructive sleep apnea
polysomnography
anthropometric features
random forest
visceral fat level
Online Access:https://www.mdpi.com/1424-8220/22/22/8630

Internet

https://www.mdpi.com/1424-8220/22/22/8630

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