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dc.contributor.authorChen, Feifan
dc.contributor.authorCao, Zuwei
dc.contributor.authorGrais, Emad M.
dc.contributor.authorZhao, Fei
dc.date.accessioned2021-01-28T10:29:06Z
dc.date.available2021-01-28T10:29:06Z
dc.date.issued2021-01-25
dc.identifier.citationChen, F., Cao, Z., Grais, E.M., Zhao, F. (2021) 'Contributions and limitations of using machine learning to predict noise-induced hearing loss', International Archives of Occupational and Environmental Health. https://doi.org/10.1007/s00420-020-01648-wen_US
dc.identifier.issn0340-0131
dc.identifier.issn1432-1246 (electronic)
dc.identifier.urihttp://hdl.handle.net/10369/11272
dc.descriptionArticle published in International Archives of Occupational and Environmental Health available open access at https://doi.org/10.1007/s00420-020-01648-wen_US
dc.description.abstractPurpose Noise-induced hearing loss (NIHL) is a global issue that impacts people’s life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction. Methods The authors searched PubMed, EMBASE and Scopus on November 26, 2020. Results Eight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve. Conclusion In spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesInternational Archives of Occupational and Environmental Health;
dc.titleContributions and limitations of using machine learning to predict noise-induced hearing lossen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1007/s00420-020-01648-w
dcterms.dateAccepted2020-12-29
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionVoRen_US
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en_US
rioxxterms.licenseref.startdate2021-01-28
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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