Reza Esmaeili1, Sajad Zare2, Fahimeh Ghasemian3, Farideh Pourtaghi4, Hamid Saeidnia5, Gholamhossein Pourtaghi6. 1. Marine Medicine Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. rezaesmaeili794@yahoo.com. 2. Department of Occupational Health Engineering and Safety at Work, Faculty of Public Health, Kerman University of Medical Sciences, Kerman, Iran. ss_zare87@yahoo.com. 3. Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Ghasemianfahime@uk.ac.ir. 4. School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. dr.f.pourtaghi@gmail.com. 5. Marine Medicine Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. hamidsaednia@gmail.com. 6. Health Research Center, Lifestyle institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. pourtaghi@bmsu.ac.ir.
Abstract
BACKGROUND: Noise-induced hearing loss (NIHL) is one of the main risk factors affecting people's health and wellbeing in the workplace. Analysing NIHL and consequently controlling the causing factors can significantly affect the improvement of working environments. Methods: One hundred and twelve male sailors participated in this study. They were classified into three groups depending on occupational noise exposure: (A) none, i.e., sound pressure level (SPL) lower than 70dBA, (B) exposed to SPL in the range of 70-85dBA, and (C) exposed to SPL exceeding 80dBA. In a first phase, hearing loss shaping risk factors were identified and analysed, including hearing loss in different frequencies, age, work experience, sound pressure level (SPL), marital status, and systolic and diastolic blood pressure. Then, neural networks were trained to predict the hearing loss changes of personnel and used to determine the weight of hearing loss factors. Finally, the accuracy of predicting models was calculated relying on Bayesian statistics. Results and conclusion: In the present study using neural networks, five models were developed. Their accuracy ranged from 92% to 100%. The frequencies of 4000Hz and 2000Hz showed the strongest association with the hearing loss of the sailors. Also, including systolic and diastolic blood pressure did not have any impact on predicted hearing loss, indicating that SPL was poorly correlated with extra-auditory effects.
BACKGROUND: Noise-induced hearing loss (NIHL) is one of the main risk factors affecting people's health and wellbeing in the workplace. Analysing NIHL and consequently controlling the causing factors can significantly affect the improvement of working environments. Methods: One hundred and twelve male sailors participated in this study. They were classified into three groups depending on occupational noise exposure: (A) none, i.e., sound pressure level (SPL) lower than 70dBA, (B) exposed to SPL in the range of 70-85dBA, and (C) exposed to SPL exceeding 80dBA. In a first phase, hearing loss shaping risk factors were identified and analysed, including hearing loss in different frequencies, age, work experience, sound pressure level (SPL), marital status, and systolic and diastolic blood pressure. Then, neural networks were trained to predict the hearing loss changes of personnel and used to determine the weight of hearing loss factors. Finally, the accuracy of predicting models was calculated relying on Bayesian statistics. Results and conclusion: In the present study using neural networks, five models were developed. Their accuracy ranged from 92% to 100%. The frequencies of 4000Hz and 2000Hz showed the strongest association with the hearing loss of the sailors. Also, including systolic and diastolic blood pressure did not have any impact on predicted hearing loss, indicating that SPL was poorly correlated with extra-auditory effects.
Authors: Arve Lie; Marit Skogstad; Håkon A Johannessen; Tore Tynes; Ingrid Sivesind Mehlum; Karl-Christian Nordby; Bo Engdahl; Kristian Tambs Journal: Int Arch Occup Environ Health Date: 2015-08-07 Impact factor: 3.015