Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm : A Field Study

• Autorzy: Zare Sajad , Ghotbiravandi Mohammad Reza , Elahishirvan Hossein , Ahsaeed Mostafa Ghazizadeh , Rostami Mina , Esmaeili Reza

Identyfikatory

DOI

10.24425/aoa.2020.133150

• Języki publikacji: EN

Abstrakty

EN

The aim of the study study was to model, with the use of a neural network algorithm, the significance of a variety of factors influencing the development of hearing loss among industry workers. The workers were categorized into three groups, according to the A-weighted equivalent sound pressure level of noise exposure: Group 1 (L_Aeq < 70 dB), Group 2 (L_Aeq 70-80 dB), and Group 3 (L_Aeq > 85 dB). The results obtained for Group 1 indicate that the hearing thresholds at the frequencies of 8 kHz and 1 kHz had the maximum effect on the development of hearing loss. In Group 2, the factors with maximum weight were the hearing threshold at 4 kHz and the worker’s age. In Group 3, maximum weight was found for the factors of hearing threshold at a frequency of 4 kHz and duration of work experience. The article also reports the results of hearing loss modeling on combined data from the three groups. The study shows that neural data mining classification algorithms can be an effective tool for the identification of hearing hazards and greatly help in designing and conducting hearing conservation programs in the industry.

Słowa kluczowe

EN

noise; modeling; NIHL; noise induced hearing loss; neural network algorithm

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2020
• Tom: Vol. 45, No. 2
• Strony: 303--311
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Zare Sajad

• Department of Occupational Health, School of Public Health, Kerman University of Medical Sciences and Health Services, Kerman, Iran

autor

Ghotbiravandi Mohammad Reza

• Department of Occupational Health, School of Public Health, Kerman University of Medical Sciences and Health Services, Kerman, Iran

autor

Elahishirvan Hossein

• Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran

autor

Ahsaeed Mostafa Ghazizadeh

• Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

autor

Rostami Mina

• Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran

autor

Esmaeili Reza

• School of Public Health, Hamedan University of Medical Sciences, Hamedan, Iran

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).