Application of Machine Learning Algorithms to Analyze the Clinical Characteristics of NIHL Caused by Impulse Noise and Steady Noise.

Background: Occupational hearing loss of workers exposed to impulse noise and workers exposed to steady noise for a long time may have different clinical characteristics. Methods: As of May 2019, all 92 servicemen working in a weapon experimental field exposed to impulse noise for over 1 year were collected as the impulse noise group. As of Dec 2019, all 78 servicemen working in an engine working experimental field exposed to steady noise for over 1 year were collected as the steady noise group. The propensity score matching (PSM) model was used to eliminate the imbalance of age and working time between the two groups of subjects. After propensity score matching, 51 subjects in each group were finally included in the study. The machine learning model is constructed according to pure tone auditory threshold, and the performance of the machine learning model is evaluated by accuracy, sensitivity, specificity, and AUC. Results: Subjects in the impulse noise group and the steady noise group had significant hearing loss at high frequencies. The hearing of the steady noise group was worse than that of the impulse noise group at speech frequency especially at the frequency of 1 kHz. Among machine learning models, XGBoost has the best prediction and classification performance. Conclusion: The pure tone auditory threshold of subjects in both groups decreased and at high frequency. The hearing of the steady noise group at 1 kHz was significantly worse than that of the impulse noise group. XGBoost is the best model to predict the classification of our two groups. Our research can guide the prevention of damage caused by different types of noises.