Risk factor assessments of temporomandibular disorders via machine learning

Abstract This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieved from the fourth Korea National Health a...

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Autores principales:	Kwang-Sig Lee, Nayansi Jha, Yoon-Ji Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/002fd6d49c5a4194905590ef993de50c
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spelling	oai:doaj.org-article:002fd6d49c5a4194905590ef993de50c2021-12-02T18:37:10ZRisk factor assessments of temporomandibular disorders via machine learning10.1038/s41598-021-98837-52045-2322https://doaj.org/article/002fd6d49c5a4194905590ef993de50c2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98837-5https://doaj.org/toc/2045-2322Abstract This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieved from the fourth Korea National Health and Nutritional Examination Survey (2009), with information concerning 4744 participants’ TMDs, demographic factors, socioeconomic status, working conditions, and health-related determinants. Based on variable importance observed from the random forest, the top 20 determinants of self-reported TMDs were body mass index (BMI), household income (monthly), sleep (daily), obesity (subjective), health (subjective), working conditions (control, hygiene, respect, risks, and workload), occupation, education, region (metropolitan), residence type (apartment), stress, smoking status, marital status, and sex. The top 20 determinants of temporomandibular disorders determined via a doctor’s diagnosis were BMI, age, household income (monthly), sleep (daily), obesity (subjective), working conditions (control, hygiene, risks, and workload), household income (subjective), subjective health, education, smoking status, residence type (apartment), region (metropolitan), sex, marital status, and allergic rhinitis. This study supports the hypothesis, highlighting the importance of obesity, general health, stress, socioeconomic status, and working conditions in the management of TMDs.Kwang-Sig LeeNayansi JhaYoon-Ji KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kwang-Sig Lee Nayansi Jha Yoon-Ji Kim Risk factor assessments of temporomandibular disorders via machine learning
description	Abstract This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieved from the fourth Korea National Health and Nutritional Examination Survey (2009), with information concerning 4744 participants’ TMDs, demographic factors, socioeconomic status, working conditions, and health-related determinants. Based on variable importance observed from the random forest, the top 20 determinants of self-reported TMDs were body mass index (BMI), household income (monthly), sleep (daily), obesity (subjective), health (subjective), working conditions (control, hygiene, respect, risks, and workload), occupation, education, region (metropolitan), residence type (apartment), stress, smoking status, marital status, and sex. The top 20 determinants of temporomandibular disorders determined via a doctor’s diagnosis were BMI, age, household income (monthly), sleep (daily), obesity (subjective), working conditions (control, hygiene, risks, and workload), household income (subjective), subjective health, education, smoking status, residence type (apartment), region (metropolitan), sex, marital status, and allergic rhinitis. This study supports the hypothesis, highlighting the importance of obesity, general health, stress, socioeconomic status, and working conditions in the management of TMDs.
format	article
author	Kwang-Sig Lee Nayansi Jha Yoon-Ji Kim
author_facet	Kwang-Sig Lee Nayansi Jha Yoon-Ji Kim
author_sort	Kwang-Sig Lee
title	Risk factor assessments of temporomandibular disorders via machine learning
title_short	Risk factor assessments of temporomandibular disorders via machine learning
title_full	Risk factor assessments of temporomandibular disorders via machine learning
title_fullStr	Risk factor assessments of temporomandibular disorders via machine learning
title_full_unstemmed	Risk factor assessments of temporomandibular disorders via machine learning
title_sort	risk factor assessments of temporomandibular disorders via machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/002fd6d49c5a4194905590ef993de50c
work_keys_str_mv	AT kwangsiglee riskfactorassessmentsoftemporomandibulardisordersviamachinelearning AT nayansijha riskfactorassessmentsoftemporomandibulardisordersviamachinelearning AT yoonjikim riskfactorassessmentsoftemporomandibulardisordersviamachinelearning
_version_	1718377778488803328

Risk factor assessments of temporomandibular disorders via machine learning

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