{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T04:56:23Z","timestamp":1768193783493,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology","award":["MOST 110-2634-F-002-049"],"award-info":[{"award-number":["MOST 110-2634-F-002-049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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, na\u00efve 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.<\/jats:p>","DOI":"10.3390\/s22228630","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T02:11:15Z","timestamp":1668046275000},"page":"8630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1639-4257","authenticated-orcid":false,"given":"Cheng-Yu","family":"Tsai","sequence":"first","affiliation":[{"name":"Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK"}]},{"given":"Huei-Tyng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK"}]},{"given":"Hsueh-Chien","family":"Cheng","sequence":"additional","affiliation":[{"name":"Parasites and Microbes Programme, Wellcome Sanger Institute, Hinxton CB10 1RQ, UK"}]},{"given":"Jieni","family":"Wang","sequence":"additional","affiliation":[{"name":"Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK"}]},{"given":"Ping-Jung","family":"Duh","sequence":"additional","affiliation":[{"name":"Cognitive Neuroscience, Division of Psychology and Language Science, University College London, London WC1H 0AP, UK"}]},{"given":"Wen-Hua","family":"Hsu","sequence":"additional","affiliation":[{"name":"School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2066-9380","authenticated-orcid":false,"given":"Marc","family":"Stettler","sequence":"additional","affiliation":[{"name":"Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9316-4976","authenticated-orcid":false,"given":"Yi-Chun","family":"Kuan","sequence":"additional","affiliation":[{"name":"Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"},{"name":"Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University, Taipei 110301, Taiwan"},{"name":"Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"}]},{"given":"Yin-Tzu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan"}]},{"given":"Chia-Rung","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"}]},{"given":"Kang-Yun","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7850-4140","authenticated-orcid":false,"given":"Jiunn-Horng","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei 110301, Taiwan"},{"name":"Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110301, Taiwan"},{"name":"Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110301, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0147-1640","authenticated-orcid":false,"given":"Dean","family":"Wu","sequence":"additional","affiliation":[{"name":"Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"},{"name":"Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University, Taipei 110301, Taiwan"},{"name":"Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-8259","authenticated-orcid":false,"given":"Hsin-Chien","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Taipei Medical University Hospital, Taipei 110301, Taiwan"}]},{"given":"Cheng-Jung","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6332-7858","authenticated-orcid":false,"given":"Arnab","family":"Majumdar","sequence":"additional","affiliation":[{"name":"Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1281-8718","authenticated-orcid":false,"given":"Wen-Te","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan"},{"name":"Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"},{"name":"Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan"},{"name":"Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110301, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"ref_1","first-page":"97","article-title":"Obstructive sleep apnea syndrome: A literature review","volume":"64","author":"Maspero","year":"2015","journal-title":"Minerva Stomatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/S2213-2600(19)30198-5","article-title":"Estimation of the global prevalence and burden of obstructive sleep apnoea: A literature-based analysis","volume":"7","author":"Benjafield","year":"2019","journal-title":"Lancet Respir. 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