Prediction of Noise-Induced Hearing Loss in the Forest Sector Using Machine Learning Techniques

Abstract: In this study, the effects of gender, age, total working time (years), working time in the sector (years), working time in a noisy environment (months), smoking, having a noisy hobby and inadequate use of ear protection equipment on noise-induced hearing loss (NIHL) were evaluated in the forest sector. The study included 1477 workers, consisting of 1247 (84.4%) males and 230 (15.6%) females. The population was aged between 18 and 60. The initial phase of the study focused on comparing regression algorithms to determine if eight independent variables contribute to NIHL in workers. The multiple linear regression algorithm was deemed the most effective in this category, yielding an R2 value of 0.3079 when tested with a data size of 25%. The second phase of the study aimed to compare classification algorithms, exploring the degree of hearing loss, measured in dB, attributed to the same eight independent variables. The dependent variable for these algorithms was categorized as “NIHL present” or “NIHL absent”. The random forest algorithm emerged as the most effective classification method, yielding an accuracy of 75% when tested with a data size of 20%. The findings of this study can guide the implementation of engineering controls to reduce noise levels, administrative controls such as limiting exposure time, and the use of personal protective equipment like hearing protection devices.

Keywords: Noise Measurement, Machine Learning, Noise-Induced Hearing Loss (NIHL), Prediction

DOI: 10.54941/ahfe1005172

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