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Titre : | Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise : A Pilot Study (2019) |
Auteurs : | Yanxia Zhao ; Jingson Li ; Meibian Zhang ; Yao Lu ; et al. |
Type de document : | Article |
Dans : | Ear and hearing (Vol. 40, n°3, mai-juin 2019) |
Article en page(s) : | p. 690-699 |
Langues: | Anglais |
Descripteurs : |
HE Vinci Exposition au bruit ; Perte d'audition |
Résumé : |
To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise.
Audiometric and noise exposure data were collected on a population of screened workers (N = 1,113) from 17 factories located in Zhejiang province, China. All the subjects were exposed to complex noise. Each subject was given an otologic examination to determine their pure-tone hearing threshold levels and had their personal full-shift noise recorded. For each subject, the hearing loss was evaluated according to the hearing impairment definition of the National Institute for Occupational Safety and Health. Age, exposure duration, equivalent A-weighted SPL (LAeq), and median kurtosis were used as the input for four machine learning algorithms, that is, support vector machine, neural network multilayer perceptron, random forest, and adaptive boosting. Both classification and regression models were developed to predict noise-induced hearing loss applying these four machine learning algorithms. Two indexes, area under the curve and prediction accuracy, were used to assess the performances of the classification models for predicting hearing impairment of workers. Root mean square error was used to quantify the prediction performance of the regression models. |
Disponible en ligne : | Oui |
En ligne : | https://login.ezproxy.vinci.be/login?url=http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=yrovftu&AN=00003446-201905000-00024 |