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

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 indep

SUBMITTER: Tsai CY 

PROVIDER: S-EPMC9694257 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature