Hearing loss prediction equation for Iranian truck drivers using neural network algorithm

Abstract

BACKGROUND: Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly. OBJECTIVE: This study aimed to predict hearing loss using an ANN algorithm and to evaluate the weight and influence of various factors affecting hearing loss among truck drivers. METHODS: A total of 692 truck drivers were selected for the study. Their occupational exposure histories were collected to identify factors influencing their hearing loss. The impact and weight of each factor were measured, and an ANN algorithm was used to model and predict the degree of hearing loss. RESULTS: The assessment of hearing loss among truck drivers revealed a prevalence of 59.98 in the right ear and 64.74 in the left ear. The most significant average hearing loss in both ears occurred at frequencies of 6000 and 8000 Hz. According to the ANN model, age and the frequency of 2000 Hz had the greatest impact on hearing loss, while sound pressure level (SPL) had the least impact. Additionally, the relationship between overall hearing loss and the type of heavy truck indicated that drivers of HOWO brand trucks experienced the highest degree of hearing loss compared to other drivers. CONCLUSIONS: This study demonstrates that the ANN algorithm is a promising tool for predicting hearing impairments caused by noise exposure among truck drivers.