Predicting Noise-Induced Hearing Loss (NIHL) and Hearing Deterioration Index (HDI) in Malaysian Industrial Workers using GDAM Algorithm

Abstract

Noise is a form of a pollutant that is terrorizing the occupational health experts for many decades due to its adverse side-effects on the workers in the industry. Noise-Induced Hearing Loss (NIHL) handicap is one out of many health hazards caused due to excessive exposure to high frequency noise emitted from the machines. A number of studies have been carried-out to find the significant factors involved in causing NIHL in industrial workers using Artificial Neural Networks. Despite providing useful information on hearing loss, these studies have neglected some important factors.  Therefore, the current study is using age, work-duration, and maximum and minimum noise exposure as the main factors involved in the hearing loss. Gradient Descent with Adaptive Momentum (GDAM) algorithm is proposed to predict the NIHL in workers. The results show 98.21% average accuracy between the actual and the predicted datasets and the MSE for both ears is 2.10x10-3. Hearing threshold shift found in the selected workers was greater than 25 dB, which means hearing impairment has occurred. Also, Hearing Deterioration Index (HDI) is found to be quite high for different sound pressure levels such as maximum exposure (dB) and average exposure (dB) but is reported normal for minimum exposure (dB) for all workers.