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

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*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

9 Scopus citations

Abstract

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 the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Original languageEnglish
Article number8630
JournalSensors
Volume22
Issue number22
DOIs
StatePublished - 11 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • anthropometric features
  • obstructive sleep apnea
  • polysomnography
  • random forest
  • visceral fat level

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