{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T20:22:04Z","timestamp":1790713324271,"version":"4.1.0"},"reference-count":66,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\"><jats:title>Importance<\/jats:title><jats:p>Sleep is critical to a person\u2019s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders.<\/jats:p><\/jats:sec><jats:sec id=\"sec002\"><jats:title>Objective<\/jats:title><jats:p>The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology.<\/jats:p><\/jats:sec><jats:sec id=\"sec003\"><jats:title>Design, setting, and participants<\/jats:title><jats:p>A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data.<\/jats:p><\/jats:sec><jats:sec id=\"sec004\"><jats:title>Methods<\/jats:title><jats:p>A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p&lt;0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia.<\/jats:p><\/jats:sec><jats:sec id=\"sec005\"><jats:title>Results<\/jats:title><jats:p>Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P&lt;0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%).<\/jats:p><\/jats:sec><jats:sec id=\"sec006\"><jats:title>Conclusion<\/jats:title><jats:p>Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.<\/jats:p><\/jats:sec>","DOI":"10.1371\/journal.pone.0282622","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T17:27:12Z","timestamp":1681320432000},"page":"e0282622","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":50,"title":["Use of machine learning to identify risk factors for insomnia"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4970-4968","authenticated-orcid":true,"given":"Alexander A.","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3663-004X","authenticated-orcid":true,"given":"Samuel Y.","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"pone.0282622.ref001","doi-asserted-by":"crossref","first-page":"e936384","DOI":"10.12659\/MSM.936384","article-title":"Association Between Sleep Duration and Sleep Disorder Data from the National Health and Nutrition Examination Survey and Stroke Among Adults in the United States","volume":"28","author":"J Wang","year":"2022","journal-title":"Med Sci Monit"},{"issue":"2","key":"pone.0282622.ref002","article-title":"Children\u2019s Environmental Health in the Digital Era: Understanding Early Screen Exposure as a Preventable Risk Factor for Obesity and Sleep Disorders.","volume":"5","author":"C Wolf","year":"2018","journal-title":"Children (Basel)."},{"issue":"7","key":"pone.0282622.ref003","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1016\/j.jstrokecerebrovasdis.2019.04.016","article-title":"A Dose-Response Relationship Between Sleep Duration and Stroke According to Nonhealth Status in Central China: A Population-based Epidemiology Survey.","volume":"28","author":"Q Guo","year":"2019","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"12","key":"pone.0282622.ref004","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1177\/2047487319861847","article-title":"Editor\u2019s Presentation Benefit of healthy lifestyle on cardiovascular risk factor control: Focus on body weight, exercise and sleep quality.","volume":"26","author":"MF Piepoli","year":"2019","journal-title":"Eur J Prev Cardiol."},{"issue":"16","key":"pone.0282622.ref005","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph191610199","article-title":"The Effects of the Exogenous Melatonin on Shift Work Sleep Disorder in Health Personnel: A Systematic Review.","volume":"19","author":"B Carriedo-Diez","year":"2022","journal-title":"Int J Environ Res Public Health"},{"key":"pone.0282622.ref006","doi-asserted-by":"crossref","first-page":"662572","DOI":"10.3389\/fpsyg.2021.662572","article-title":"The Epidemiology of Insomnia and Sleep Duration Across Mental and Physical Health: The SHoT Study.","volume":"12","author":"B Sivertsen","year":"2021","journal-title":"Front Psychol."},{"issue":"6892","key":"pone.0282622.ref007","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1136\/bmj.306.6892.1604","article-title":"ABC of sleep disorders. 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