Abstract
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. Roo...Continue Reading
References
Jun 1, 1993·Ear and Hearing·D HendersonF A Boettcher
Jun 6, 1998·Neural Computation·M Pontil, A Verri
Nov 21, 2000·Journal of Microbiological Methods·I A Basheer, M Hajmeer
Jan 12, 2002·The Journal of the Acoustical Society of America·R P Hamernik, W Qiu
Dec 25, 2002·Occupational and Environmental Medicine·T MizoueT Shimizu
Jul 26, 2003·The Journal of the Acoustical Society of America·Roger P HamernikBob Davis
Mar 12, 2005·International Archives of Occupational and Environmental Health·Kyoko NomuraEiji Yano
Jan 18, 2007·The Journal of the Acoustical Society of America·Wei QiuBob Davis
Apr 6, 2007·The Journal of the Acoustical Society of America·Wei QiuRoger P Hamernik
Sep 13, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ajmal S MianRobyn Owens
Feb 7, 2008·IEEE Transactions on Neural Networks·H DruckerV N Vapnik
Feb 6, 2009·Ear and Hearing·Annelies KoningsGuy Van Camp
Apr 3, 2009·Development and Psychopathology·David H BarkerUNKNOWN CDaCI Investigative Team
Aug 7, 2009·Ear and Hearing·Robert I DavisRoger P Hamernik
Jul 1, 2010·Ear and Hearing·Yi-Ming ZhaoRoger P Hamernik
May 10, 2013·The Journal of the Acoustical Society of America·Wei QiuRobert I Davis
Nov 30, 2014·International Archives of Occupational and Environmental Health·Mohsen AliabadiEbrahim Darvishi
Aug 8, 2015·International Archives of Occupational and Environmental Health·Arve LieKristian Tambs
Sep 12, 2015·Noise & Health·Armando Carballo PelegrinMaria Pilar Arévalo Morales
Oct 27, 2015·Indian Journal of Occupational and Environmental Medicine·Maryam FarhadianEbrahim Darvishi
Dec 17, 2015·Ear and Hearing·Hong-Wei XieRoger P Hamernik
Citations
Nov 27, 2019·Genes·Jade HotchkissGaston K Mazandu
Mar 12, 2020·Clinical and Experimental Otorhinolaryngology·Keon Vin ParkJune Choi
Jun 10, 2020·Otolaryngology--head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery·Eunice YouMatthew G Crowson
Aug 9, 2019·BMC Geriatrics·Min ZhangZhijun Bao
Jan 26, 2021·International Archives of Occupational and Environmental Health·Feifan ChenFei Zhao
Apr 13, 2021·Frontiers in Medicine·Qixuan WangHao Wu
May 5, 2021·The Journal of the Acoustical Society of America·Yu TianWei Qiu
Feb 12, 2020·Environmental Pollution·Komodo MattaGerman Cano-Sancho
May 20, 2021·Ear and Hearing·Zhihao ShiMeibian Zhang
Nov 25, 2021·Trends in Hearing·Qin GongHonghao Yang