ArticleInternational archives of occupational and environmental health2015
Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.
Article in International archives of occupational and environmental health, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Recent advances in hearing conservation programmes: A systematic review.The South African journal of communication disorders = Die Suid-Afrikaanse tydskrif vir Kommunikasieafwykings · 2020Pooled it
- Applying Machine Learning Models to Predict Occupational Noise-Induced Hearing Loss: A Systematic Review and Meta-analysis.Noise & healthPooled it
- The combined effects of occupational exposure to noise and other risk factors - a systematic review.Noise & healthPooled it
- Article
- Noise-induced hearing loss and blood factors: results from a comprehensive cross-sectional study in Iran.BMC public health · 2025Article
- Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program.Safety and health at work · 2024Article
- Article
- Occupational Noise-Induced Hearing Loss in Iran: A Systematic Review and Meta-Analysis.Iranian journal of public health · 2023Review
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Proposal of Combined Noise and Hand-Arm Vibration Index for Occupational Exposure: Application to a Study Case in the Olive Sector.International journal of environmental research and public health · 2022Article
- Predicting and classifying hearing loss in sailors working on speed vessels using neural networks: a field study.La Medicina del lavoro · 2022Article
- Prediction of hearing recovery in unilateral sudden sensorineural hearing loss using artificial intelligence.Scientific reports · 2022Article
- Contributions and limitations of using machine learning to predict noise-induced hearing loss.International archives of occupational and environmental health · 2021Review
- A risk model and nomogram for high-frequency hearing loss in noise-exposed workers.BMC public health · 2021Article
- Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers.PeerJ. Computer science · 2021Article
- Prediction of absenteeism in public schools teachers with machine learning.Revista de saude publica · 2021Article
- Comparison of data mining algorithms for sex determination based on mastoid process measurements using cone-beam computed tomography.Imaging science in dentistry · 2020Article
- Diagnosing thyroid disorders: Comparison of logistic regression and neural network models.Journal of family medicine and primary care · 2020Article
- A parsimonious approach for screening moderate-to-profound hearing loss in a community-dwelling geriatric population based on a decision tree analysis.BMC geriatrics · 2019Article
- Individual Fit Testing of Hearing Protection Devices Based on Microphone in Real Ear.Safety and health at work · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePrediction 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.
methodsTwo 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.
resultsThe 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.
conclusionsSince 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.