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

Author:

Tsai Cheng-YuORCID,Huang Huei-Tyng,Cheng Hsueh-Chien,Wang Jieni,Duh Ping-Jung,Hsu Wen-Hua,Stettler MarcORCID,Kuan Yi-ChunORCID,Lin Yin-Tzu,Hsu Chia-Rung,Lee Kang-Yun,Kang Jiunn-HorngORCID,Wu DeanORCID,Lee Hsin-ChienORCID,Wu Cheng-Jung,Majumdar ArnabORCID,Liu Wen-TeORCID

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.

Funder

Ministry of Science and Technology

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference50 articles.

1. Obstructive sleep apnea syndrome: A literature review;Maspero;Minerva Stomatol.,2015

2. Estimation of the global prevalence and burden of obstructive sleep apnoea: A literature-based analysis;Benjafield;Lancet Respir. Med.,2019

3. Increased prevalence of sleep-disordered breathing in adults;Peppard;Am. J. Epidemiol.,2013

4. Diagnosis and management of obstructive sleep apnea: A review;Gottlieb;JAMA,2020

5. Neurocognitive impairment in obstructive sleep apnea;Lal;Chest,2012

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3