Prediction of Hearing Loss Among the Noise-exposed Workers in a Steel Factory Using Artificial Intelligence Approach

Overview

Journal Int Arch Occup Environ Health

Specialty Environmental Health

Date 2014 Nov 30

PMID 25432298

Citations 19

Authors

Mohsen Aliabadi

Maryam Farhadian

Ebrahim Darvishi

Affiliations

Soon will be listed here.

Abstract

Purpose: 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.

Citing Articles

Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program.

Khajonklin T, Sun Y, Guo Y, Hsu H, Yoon C, Lin C Saf Health Work. 2024; 15(2):220-227.

PMID: 39035795 PMC: 11255955. DOI: 10.1016/j.shaw.2024.02.004.

Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population.

Ruan Y, Huang G, Zhang J, Mai S, Gu C, Rong X BMJ Open. 2024; 14(5):e079955.

PMID: 38760055 PMC: 11103207. DOI: 10.1136/bmjopen-2023-079955.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.

Okeibunor J, Jaca A, Iwu-Jaja C, Idemili-Aronu N, Ba H, Zantsi Z Front Public Health. 2023; 11:1102185.

PMID: 37469694 PMC: 10352788. DOI: 10.3389/fpubh.2023.1102185.

Occupational Noise-Induced Hearing Loss in Iran: A Systematic Review and Meta-Analysis.

Etemadinezhad S, Sammak Amani A, Moosazadeh M, Rahimlou M, Samaei S Iran J Public Health. 2023; 52(2):278-289.

PMID: 37089160 PMC: 10113583. DOI: 10.18502/ijph.v52i2.11881.

Proposal of Combined Noise and Hand-Arm Vibration Index for Occupational Exposure: Application to a Study Case in the Olive Sector.

Nieto-Alvarez R, de la Hoz-Torres M, Aguilar A, Martinez-Aires M, Ruiz D Int J Environ Res Public Health. 2022; 19(21).

PMID: 36361218 PMC: 9654875. DOI: 10.3390/ijerph192114345.

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