Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.
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 an...
| Publicado en: | Work Vol. 80; no. 4; pp. 1684 - 1696 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184955675&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184955675 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: Apr2025 vid: 80 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 184955675 181127756 184955675 184955675 10.1177/10519815241295943 184955675 ppf: 1684 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Hearing loss prediction equation for Iranian truck drivers using neural network algorithm. aug: au: Esmaeili, Reza Aghapanah, Hamed Ghasemian, Hamideh Ashouri, Maryam Nakhaei pour, Mojtaba Alboghobeish, Ali jalali, Mahdi Ghotbi, Negar Esmaeili, Sayed Vahid affil: Student Research Committee, Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran sug: subj: Hearing Loss, Noise-Induced Epidemiology Hearing Loss, Noise-Induced Diagnosis Prediction Models Truck Drivers Neural Networks (Computer) Algorithms Early Diagnosis Human Iran Occupational Exposure Adverse Effects Hearing Loss, Noise-Induced Risk Factors Descriptive Statistics Analytic Research Cross Sectional Studies ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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