{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T20:15:06Z","timestamp":1790712906231,"version":"4.1.0"},"reference-count":79,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3\u20137% of males and 2\u20135% of females. In the United States alone, 50\u201370 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.<\/jats:p>","DOI":"10.3390\/diagnostics13142417","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T04:27:13Z","timestamp":1689827233000},"page":"2417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-6276","authenticated-orcid":false,"given":"Alaa","family":"Sheta","sequence":"first","affiliation":[{"name":"Computer Science Department, Southern Connecticut State University, New Haven, CT 06514, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7047-5886","authenticated-orcid":false,"given":"Thaer","family":"Thaher","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering, Arab American University, Jenin P.O. Box 240, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7105-4266","authenticated-orcid":false,"given":"Salim R.","family":"Surani","sequence":"additional","affiliation":[{"name":"Department of Pulmonary, Critical Care & Sleep Medicine, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8103-563X","authenticated-orcid":false,"given":"Hamza","family":"Turabieh","sequence":"additional","affiliation":[{"name":"Health Management and Informatics Department, School of Medicine, University of Missouri, Columbia, MO 65212, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4180-3734","authenticated-orcid":false,"given":"Malik","family":"Braik","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al-Balqa Applied University, Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-1038","authenticated-orcid":false,"given":"Jingwei","family":"Too","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5260-7734","authenticated-orcid":false,"given":"Noor","family":"Abu-El-Rub","sequence":"additional","affiliation":[{"name":"Center of Medical Informatics and Enterprise Analytics, University of Kansas Medical Center, Kansas City, KS 66160, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0387-8252","authenticated-orcid":false,"given":"Majdi","family":"Mafarjah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Birzeit University, Birzeit P.O. Box 14, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2794-8144","authenticated-orcid":false,"given":"Hamouda","family":"Chantar","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Sebha University, Sebha 18758, Libya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shyam","family":"Subramanian","sequence":"additional","affiliation":[{"name":"Pulmonary, Critical Care & Sleep Medicine, Sutter Health, Tracy, CA 95376, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"ref_1","unstructured":"Dickens, C. (1837). The Posthumous Papers of the Pickwick Club, Chapman and Hall. 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