{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T19:49:03Z","timestamp":1791834543208,"version":"4.3.4"},"reference-count":23,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100004484","name":"Tehran University of Medical Sciences and Health Services","doi-asserted-by":"publisher","award":["1404-6-294-91508"],"award-info":[{"award-number":["1404-6-294-91508"]}],"id":[{"id":"10.13039\/501100004484","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["WORK: A Journal of Prevention, Assessment &amp; Rehabilitation"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pure-tone audiometry is the gold standard for diagnosing occupational noise-induced hearing loss (ONIHL); however, its reliance on expensive equipment, trained personnel, and subjective auditory responses limits its use in large-scale occupational screening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and compare machine learning (ML) algorithms for ONIHL classification using audiometric data alone and audiometric data combined with demographic and clinical features, and to assess whether these variables provide additional predictive value beyond audiometric thresholds alone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this cross-sectional study of 1524 workers employed in the Iranian petrochemical industry, four ML algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were developed to classify ONIHL. Data were split 80:20 into training and testing sets. Model performance was evaluated using accuracy, precision, sensitivity, F1-score, and the kappa statistic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Tree-based models outperformed SVM across ONIHL levels. Adding demographic-clinical variables improved accuracy, sensitivity, and F1-score by 2\u20135%. All models achieved 100% precision for normal hearing. For moderate-to-severe loss, XGBoost achieved 85% precision and 89% sensitivity, whereas RF achieved 81% and 87%, respectively. SVM performed less well for mild loss, likely because of sensitivity to class imbalance. The 6000\u2005Hz threshold was the most important feature across models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML algorithms, particularly tree-based models, effectively identified ONIHL. Combining audiometric and demographic-clinical data enhanced performance and may support more accurate screening and timely intervention in high-risk occupational populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10519815261492237","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T12:11:36Z","timestamp":1791634296000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing the performance of machine learning algorithms in classifying occupational noise-induced hearing loss"],"prefix":"10.1177","author":[{"given":"Vida","family":"Rezaei-Hachesu","sequence":"first","affiliation":[{"name":"Tehran University of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shadi","family":"Naderyan Fe\u2032li","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran"},{"name":"Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Monazzam-Esmaeelpour","sequence":"additional","affiliation":[{"name":"Tehran University of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"askari","sequence":"additional","affiliation":[{"name":"Tehran University of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3689-2699","authenticated-orcid":false,"given":"Ali","family":"Jafari","sequence":"additional","affiliation":[{"name":"Tehran University of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/s43163-025-00790-x"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1017\/S002221512400094X"},{"key":"e_1_3_3_4_2","first-page":"133","article-title":"Relationship among noise exposure, noise annoyance, emotional intelligence, and cognitive emotional regulation: a generalized structural equation modeling","volume":"33","author":"Madvari RF","year":"2024","unstructured":"Madvari RF, Abrchi A, Sefidkar R, et al. Relationship among noise exposure, noise annoyance, emotional intelligence, and cognitive emotional regulation: a generalized structural equation modeling. Aud Vestib Res 2024; 33: 133\u2013141.","journal-title":"Aud Vestib Res"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00420-014-1004-z"},{"key":"e_1_3_3_6_2","article-title":"Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study","volume":"15","author":"Li C","year":"2025","unstructured":"Li C, Shi L, Chen L, et al. Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study. 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Age-related hearing loss: an updated and comprehensive review of the interventions. 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