Utilizing Artificial Neural Networks for Establishing Hearing-Loss
Predicting Models Based on a Longitudinal Dataset and Their
Implications for Managing the Hearing Conservation Program

Utilizing Artificial Neural Networks for Establishing Hearing-Loss
Predicting Models Based on a Longitudinal Dataset and Their
Implications for Managing the Hearing Conservation Program

Artificial neural network
Cross-sectional data
Longitudinal data
Noise-induced hearing losses
Predicting model

Though the artificial neural network (ANN) technique has been used to predict noiseinduced hearing loss (NIHL), the established prediction models have primarily relied on crosssectional datasets, and hence, they may not comprehensively capture the chronic nature of NIHL as a
disease linked to long-term noise exposure among workers.
Methods: A comprehensive dataset was utilized, encompassing eight-year longitudinal personal hearing
threshold levels (HTLs) as well as information on seven personal variables and two environmental
variables to establish NIHL predicting models through the ANN technique. Three subdatasets were
extracted from the afirementioned comprehensive dataset to assess the advantages of the present study
in NIHL predictions.
Results: The dataset was gathered from 170 workers employed in a steel-making industry, with a median
cumulative noise exposure and HTL of 88.40 dBA-year and 19.58 dB, respectively. Utilizing the longitudinal dataset demonstrated superior prediction capabilities compared to cross-sectional datasets.
Incorporating the more comprehensive dataset led to improved NIHL predictions, particularly when
considering variables such as noise pattern and use of personal protective equipment. Despite fluctuations observed in the measured HTLs, the ANN predicting models consistently revealed a discernible
trend.
Conclusions: A consistent correlation was observed between the measured HTLs and the results obtained
from the predicting models. However, it is essential to exercise caution when utilizing the modelpredicted NIHLs for individual workers due to inherent personal fluctuations in HTLs. Nonetheless,
these ANN models can serve as a valuable reference for the industry in effectively managing its hearing
conservation program.

Thanawat Khajonklin 1
, Yih-Min Sun 2
, Yue-Liang Leon Guo 3
, Hsin-I Hsu 4
,
Chung Sik Yoon 5
, Cheng-Yu Lin 6,q, Perng-Jy Tsai

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1Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
2Department of Occupational Safety and Health, Chung Hwa University of Medical Technology, Tainan County, Taiwan
3Department of Environmental and Occupational Medicine, Medical College, National Taiwan University, Taipei City, Taiwan
4 Environmental and Labor Affairs Division, Southern Taiwan Science Park Bureau, Ministry of Science and Technology, Tainan City, Taiwan
5Department of Environmental Health Sciences, Seoul National University Graduate School of Public Health, Seoul, Republic of Korea
6Department of Otolaryngology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan City, Taiwan

20 February 2024

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