Optimizing a Classification Model to Evaluate Individual Susceptibility to Noise-induced Hearing Loss (Preprint)

BACKGROUND Noise-induced hearing loss (NIHL), one of the leading causes of hearing loss in young adults, is a major health care problem that has negative social and economic consequences. Among those exposed to the detrimental effects of workplace noise, some are especially vulnerable and develop NIHL after only a short time in the workplace, while other are extremely resistant and develop NIHL after many years of work. OBJECTIVE To determine an optimal model for detecting susceptible/resistant to NIHL and further explore phenotypic traits uniquely associated with susceptibility profiles. METHODS This study was performed from 2015 to 2021 at shipyards in Shanghai, China. Six methods were applied to our dataset to evaluated their classification performance. An machine learning (ML)-based diagnostic model employing frequencies from 0.25 to 12 kHz were developed to determine the most reliable frequencies, considering accuracy and area under the curve (AUC). An optimal method with most reliable frequencies was then constructed for detecting susceptible/resistant to NIHL. Phenotypic characteristics such as age, exposure time, cumulative noise exposure (CNE), and HTs, were explored for these group. RESULTS A total of 6276 participants (median interquartile range (IQR) age, 41.0 33.0-47.0 years; 5372 84.9% men) were included in the analysis. An ML-based NIHL diagnostic model with misclassified subjects showed the most promising performance for identifying workers in NIHL susceptible group (NIHL-SG) and NIHL resistant group (NIHL-RG). The mean hearing thresholds (HTs) at 4 and 12.5 kHz frequencies demonstrates the highest predictive value for detecting NIHL-SG and NIHL-RG (accuracy = 0.78, AUC = 0.81). Individuals in the NIHL-SG selected by the optimized model were younger (28.0 25.0-31.0 versus 35.0 32.0-39.0 years old, p CONCLUSIONS An ML-based NIHL diagnostic model with misclassified subjects, employing the mean HTs of 4 and 12.5kHz, was regarded as the most reliable method for identifying individuals susceptible or resistant to NIHL, proceeding with future studies on genetic that govern susceptibility to NIHL. CLINICALTRIAL This study was granted by the Institutional Ethics Review Board at Shanghai Sixth People’s Hospital affiliated with Shanghai Jiao Tong University and was registered in the Chinese Clinical Trial Registry under the identifier ChiCTR-RPC-17012580.