Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study - ZARE, Sajad; GHOTBIRAVANDI, Mohammad Reza; ELAHISHIRVAN, Hossein; AHSAEED, Mostafa Ghazizadeh; ROSTAMI, Mina; ESMAEILI, Reza (Archives of Acoustics, 2020)
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 (LAeq < 70 dB), Group 2 (LAeq 70–80 dB), and Group 3 (LAeq > 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.