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Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss
Published on: January 25, 2016
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Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.
Reza Esmaeili1, Hamed Aghapanah2, Hamideh Ghasemian3
1Student Research Committee, Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Work (Reading, Mass.)
|May 8, 2025
Summary
Artificial neural networks (ANNs) can predict hearing loss in truck drivers. Age and specific sound frequencies significantly impact hearing, aiding early detection and management.
Area of Science:
- Occupational Health
- Audiology
- Data Science
Background:
- Truck drivers face a high prevalence of hearing loss.
- Early detection of hearing impairment is crucial for effective management.
- Artificial Neural Networks (ANNs) offer potential for predictive modeling in this domain.
Purpose of the Study:
- To predict hearing loss in truck drivers using an ANN algorithm.
- To evaluate the influence and weight of various factors contributing to hearing loss.
- To identify specific risk factors within the truck driving profession.
Main Methods:
- A cohort of 692 truck drivers was studied.
- Occupational exposure histories were collected and analyzed.
- An ANN model was developed to predict hearing loss severity.
Main Results:
- Hearing loss prevalence was 59.98% (right ear) and 64.74% (left ear).
- Highest hearing loss occurred at 6000 and 8000 Hz frequencies.
- Age and 2000 Hz frequency showed the greatest impact; Sound Pressure Level (SPL) had the least.
Conclusions:
- ANNs show promise for predicting noise-induced hearing loss in truck drivers.
- The study identified key risk factors, including truck brand (HOWO drivers highest risk).
- Predictive modeling can support targeted interventions for occupational hearing health.
