Hearing loss is highly prevalent in occupational populations, but it could be effectively prevented through early monitoring. There is still a lack of studies on the risk assessment of hearing loss.

To construct three risk assessment models for hearing loss in oil workers, and evaluate their performance to obtain the optimal one.

A cross-sectional study was conducted. Participants were 1 423 workers of an oil company who received the occupational health examination from 2018 to 2019 in the Jingxia Hospital of North China Petroleum Administration. Their general demographic data, audiometric test and laboratory test results were collected. Unconditional multivariable Logistic regression was used to explore the factors influencing hearing loss. Python was used to build the random forest, XG Boost, and BP neural network models with factors potentially associated with hearing loss determined based on a literature review and expert opinions incorporated. The discriminative ability of the models were evaluated using the receiver operating characteristic curve (ROC) , and the calibration ability of the model was tested using the calibration curve.

The prevalence of hearing loss changed significantly according to age, gender, monthly household income, history of diabetes, labor intensity, physical exercise, ototoxic chemical exposure, sleep disturbance, shift, and high temperature exposure (P<0.05) . The prevalence of hearing loss rose with the increase in years of work and cumulative noise exposure (P<0.05) . The results of unconditional multivariate Logistic regression analysis showed that 50- years old, diabetes, ototoxic, chemical exposure, insomnia, shift, 30-years of work and cumulative noise exposure≥90 dB (A) ·year were risk factors for hearing loss in oil workers (P<0.05) , monthly household income≥11 000 and moderate labor intensity were protective factors for hearing loss in oil workers (P<0.05) . The AUC of the random forest in assessing hearing loss risk in oil workers was 0.95, with 95.99% accuracy, 91.43% sensitivity, 97.69% specificity, a Youden index of 0.89 and a F1 score of 0.74, the AUC of the XG Boost model in assessing hearing loss risk in oil workers was 0.93, with 95.22% accuracy, 89.09% sensitivity, 97.50% specificity, a Youden index of 0.87 and a F1 score of 0.73, and that of the BP neural network model in assessing hearing loss risk in oil workers was 0.83, with 88.62% accuracy, 70.13% sensitivity, 95.47% specificity, a Youden index of 0.66 and a F1 score of 0.73. The Brier score of the random forest was 0.04, with an observation-to-expectation (O/E) ratio of 1.02 and a calibration-in-the-large of 0.029. The Brier score, O/E ratio and calibration-in-the-large of the XG Boost model were 0.04, 1.04 and 0.032, respectively. The Brier score of the BP neural network model was 0.11, with an O/E ratio of 1.21 and a calibration-in-the-large of 0.097. The calibration efficiency of the random forest model was the best.

The random forest model outperformed the XG Boost model and the BP neural network model, which could be adopted to assess the risk of hearing loss in oil workers more accurately.