title: Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.
abstract: OBJECTIVE
  Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using artificial neural networks, this study aims to present an empirical model for the prediction of the hearing loss threshold among noise-exposed workers.
  
  
  METHODS
  Two hundred and ten workers employed in a steel factory were chosen, and their occupational exposure histories were collected. To determine the hearing loss threshold, the audiometric test was carried out using a calibrated audiometer. The personal noise exposure was also measured using a noise dosimeter in the workstations of workers. Finally, data obtained five variables, which can influence the hearing loss, were used for the development of the prediction model. Multilayer feed-forward neural networks with different structures were developed using MATLAB software. Neural network structures had one hidden layer with the number of neurons being approximately between 5 and 15 neurons.
  
  
  RESULTS
  The best developed neural networks with one hidden layer and ten neurons could accurately predict the hearing loss threshold with RMSE = 2.6 dB and R(2) = 0.89. The results also confirmed that neural networks could provide more accurate predictions than multiple regressions.
  
  
  CONCLUSIONS
  Since occupational hearing loss is frequently non-curable, results of accurate prediction can be used by occupational health experts to modify and improve noise exposure conditions.
authors.affiliation: Department of Occupational Health, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran, mohsen.aliabadi@umsha.ac.ir.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Aliabadi
authors.forename: Maryam
authors.initials: M
authors.lastname: Farhadian
authors.forename: Ebrahim
authors.initials: E
authors.lastname: Darvishi
mesh_terms: D000328:Adult; D001299:Audiometry / Q000379:methods; D001309:Auditory Threshold; D003430:Cross-Sectional Studies; D006317:Hearing Loss, Noise-Induced / Q000209:etiology*; D006801:Humans; D007492:Iran; D008297:Male; D008669:Metallurgy; D016571:Neural Networks, Computer; D009623:Noise, Occupational / Q000009:adverse effects*; D009784:Occupational Diseases / Q000209:etiology*; D016273:Occupational Exposure / Q000009:adverse effects* / Q000032:analysis; D011237:Predictive Value of Tests; D013232:Steel
chemical_list: D013232:Steel