Application of Machine Learning Algorithms to Analyze the Clinical Characteristics of NIHL Caused by Impulse Noise and Steady Noise
Abstract
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.
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14. Chen SM, Fan YT, Martinez RM, Chen CY (2023). Noise-induced hearing loss profile among Taiwan Airforce on duty pilots. Am J Otolaryngol, 44(3):103802.
15. Servi AT, Davis SK, Murphy SA, et al (2022). Prospective measurements of hearing threshold during military rifle training with in-ear, protected, noise exposure monitoring. J Acoust Soc Am, 152 (4):2257.
16. Doupe P, Faghmous J, Basu S (2019). Machine Learning for Health Services Researchers. Value Health, 22 (7):808-815.
17. Guan X, Zhang B, Fu M, et al (2021). Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Ann Med, 53 (1):257-266.
18. Basner M, Babisch W, Davis A, et al (2014). Auditory and non-auditory effects of noise on health. Lancet, 383 (9925):1325-1332.
19. Zaman M, Muslim M, Jehangir A (2022). Environmental noise-induced cardiovascular, metabolic and mental health disorders: a brief review. Environ Sci Pollut Res Int, 29(51):76485-76500.
20. Cui B, Wu MG, She XJ, et al (2012). Impulse noise exposure in rats causes cognitive deficits and changes in hippocampal neurotransmitter signaling and tau phosphorylation. Brain Res, 1427:35-43.
21. Miguel V, Cui JY, Daimiel L, et al (2018). The Role of MicroRNAs in Environmental Risk Factors, Noise-Induced Hearing Loss, and Mental Stress. Antioxid Redox Signal, 28 (9):773-796.
22. Malowski KS, Gollihugh LH, Malyuk H, et al (2022). Auditory changes following firearm noise exposure, a review. J Acoust Soc Am, 151 (3):1769.
23. Bing D, Ying J, Miao J, et al (2018). Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol, 43 (3):868-874.
24. Tomiazzi JS, Pereira DR, Judai MA, et al (2019). Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke. Environ Sci Pollut Res Int, 26(7):6481-6491.
25. McBride DI, Williams S (2001). Audiometric notch as a sign of noise induced hearing loss. Occup Environ Med, 58 (1):46-51.
26. Narasimhan S, Rajagopalan R, Margret JJ, et al (2022). Audiometric notch as a sign of noise induced hearing loss (NIHL) among the rice and market flour mill workers in Tamil Nadu, South India. Hearing Balance and Communication, 20 (1):21-31.
27. Moon IS, Park SY, Park HJ, et al (2011). Clinical Characteristics of Acoustic Trauma Caused by Gunshot Noise in Mass Rifle Drills without Ear Protection. J Occup Environ Hyg, 8 (10):618-623.
28. Bajin MD, Dahm V, Lin VYW (2022). Hidden hearing loss: current concepts. Curr Opin Otolaryngol Head Neck Surg, 30 (5):321-325.
29. Ryan AF, Kujawa SG, Hammill T, et al (2016). Temporary and Permanent Noise-induced Threshold Shifts: A Review of Basic and Clinical Observations. Otol Neurotol, 37 (8):e271-e275.
30. Papa JP, Falcao AX, de Albuquerque VHC, Tavares JMRS (2012). Efficient supervised optimum-path forest classification for large datasets. Pattern Recognition, 45 (1):512-520.
31. Chen H, Hu L, Li H, et al (2017). An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes. Basic Clin Pharmacol Toxicol, 120 (1):86-96.
32. Wang XM, Chen XH, Wang QG, Chen GN (2022). Early Diagnosis of Parkinson's Disease with Speech Pronunciation Features Based on XGBoost Model. 2022 2nd Ieee International Conference on Software Engineering and Artificial Intelligence (Seai 2022):209-213.
33. Mo XL, Chen XJ, Leong CF, et al(2020). Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning. Front Pharmacol, 11: 1164.
2. Tufts JB, Weathersby PK, Marshall L (2009). Estimation of Equivalent Noise Exposure Level Using Hearing Threshold Levels of a Population. Ear Hear, 30 (2):287-290.
3. Yuan BC, Su FM, Wu WT, et al (2012). A predictive model of the association between gene polymorphism and the risk of noise-induced hearing loss caused by gunfire noise. J Chin Med Assoc, 75 (1):36-9.
4. Theodoroff SM, Konrad-Martin D (2020). Noise: Acoustic Trauma and Tinnitus, the US Military Experience. Otolaryngol Clin North Am, 53 (4):543-553.
5. Bramhall NF, Konrad-Martin D, McMillan GP, Griest SE (2017). Auditory Brainstem Response Altered in Humans With Noise Exposure Despite Normal Outer Hair Cell Function. Ear Hear, 38 (1):e1-e12.
6. Bainbridge KE, Wallhagen MI (2014). Hearing Loss in an Aging American Population: Extent, Impact, and Management. Annu Rev Public Health, 35:139-152.
7. Themann CL, Masterson EA (2019). Occupational noise exposure: A review of its effects, epidemiology, and impact with recommendations for reducing its burden. J Acoust Soc Am, 146 (5):3879.
8. Clifford RE, Ryan AF, Program VMV (2022). The Interrelationship of Tinnitus and Hearing Loss Secondary to Age, Noise Exposure, and Traumatic Brain Injury. Ear Hear, 43 (4):1114-1124.
9. Xiong M, Yang CH, Lai HW, Wang J (2014). Impulse noise exposure in early adulthood accelerates age-related hearing loss. Eur Arch Otorhinolaryngol, 271 (6):1351-1354.
10. Kim S, Lim EJ, Kim TH, Park JH (2017). Long-term effect of noise exposure during military service in South Korea. Int J Audiol, 56 (2):130-136.
11. Lewis MS, Reavis KM, Griest S, et al(2023). The influence of tinnitus and hearing loss on the functional status of military Service members and Veterans. Int J Audiol, 62(1):44-52.
12. Zare S, Ghotbi-Ravandi MR, ElahiShirvan H, et al (2019). Predicting and Weighting the Factors Affecting Workers' Hearing Loss Based on Audiometric Data Using C5 Algorithm. Ann Glob Health, 85 (1):88.
13. ElahiShirvan H, Ghotbi-Ravandi M, Zare S, Ahsaee MG (2020). Using Audiometric Data to Weigh and Prioritize Factors that Affect Workers' Hearing Loss through Support Vector Machine (SVM) Algorithm. Sound and Vibration, 54 (2):99-112.
14. Chen SM, Fan YT, Martinez RM, Chen CY (2023). Noise-induced hearing loss profile among Taiwan Airforce on duty pilots. Am J Otolaryngol, 44(3):103802.
15. Servi AT, Davis SK, Murphy SA, et al (2022). Prospective measurements of hearing threshold during military rifle training with in-ear, protected, noise exposure monitoring. J Acoust Soc Am, 152 (4):2257.
16. Doupe P, Faghmous J, Basu S (2019). Machine Learning for Health Services Researchers. Value Health, 22 (7):808-815.
17. Guan X, Zhang B, Fu M, et al (2021). Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Ann Med, 53 (1):257-266.
18. Basner M, Babisch W, Davis A, et al (2014). Auditory and non-auditory effects of noise on health. Lancet, 383 (9925):1325-1332.
19. Zaman M, Muslim M, Jehangir A (2022). Environmental noise-induced cardiovascular, metabolic and mental health disorders: a brief review. Environ Sci Pollut Res Int, 29(51):76485-76500.
20. Cui B, Wu MG, She XJ, et al (2012). Impulse noise exposure in rats causes cognitive deficits and changes in hippocampal neurotransmitter signaling and tau phosphorylation. Brain Res, 1427:35-43.
21. Miguel V, Cui JY, Daimiel L, et al (2018). The Role of MicroRNAs in Environmental Risk Factors, Noise-Induced Hearing Loss, and Mental Stress. Antioxid Redox Signal, 28 (9):773-796.
22. Malowski KS, Gollihugh LH, Malyuk H, et al (2022). Auditory changes following firearm noise exposure, a review. J Acoust Soc Am, 151 (3):1769.
23. Bing D, Ying J, Miao J, et al (2018). Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol, 43 (3):868-874.
24. Tomiazzi JS, Pereira DR, Judai MA, et al (2019). Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke. Environ Sci Pollut Res Int, 26(7):6481-6491.
25. McBride DI, Williams S (2001). Audiometric notch as a sign of noise induced hearing loss. Occup Environ Med, 58 (1):46-51.
26. Narasimhan S, Rajagopalan R, Margret JJ, et al (2022). Audiometric notch as a sign of noise induced hearing loss (NIHL) among the rice and market flour mill workers in Tamil Nadu, South India. Hearing Balance and Communication, 20 (1):21-31.
27. Moon IS, Park SY, Park HJ, et al (2011). Clinical Characteristics of Acoustic Trauma Caused by Gunshot Noise in Mass Rifle Drills without Ear Protection. J Occup Environ Hyg, 8 (10):618-623.
28. Bajin MD, Dahm V, Lin VYW (2022). Hidden hearing loss: current concepts. Curr Opin Otolaryngol Head Neck Surg, 30 (5):321-325.
29. Ryan AF, Kujawa SG, Hammill T, et al (2016). Temporary and Permanent Noise-induced Threshold Shifts: A Review of Basic and Clinical Observations. Otol Neurotol, 37 (8):e271-e275.
30. Papa JP, Falcao AX, de Albuquerque VHC, Tavares JMRS (2012). Efficient supervised optimum-path forest classification for large datasets. Pattern Recognition, 45 (1):512-520.
31. Chen H, Hu L, Li H, et al (2017). An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes. Basic Clin Pharmacol Toxicol, 120 (1):86-96.
32. Wang XM, Chen XH, Wang QG, Chen GN (2022). Early Diagnosis of Parkinson's Disease with Speech Pronunciation Features Based on XGBoost Model. 2022 2nd Ieee International Conference on Software Engineering and Artificial Intelligence (Seai 2022):209-213.
33. Mo XL, Chen XJ, Leong CF, et al(2020). Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning. Front Pharmacol, 11: 1164.
| Files | ||
| Issue | Vol 53 No 7 (2024) | |
| Section | Original Article(s) | |
| Keywords | ||
| Noise-induced hearing loss Impulse noise Steady noise Machine learning | ||
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How to Cite
1.
Fan B, Wang G, Wu W. Application of Machine Learning Algorithms to Analyze the Clinical Characteristics of NIHL Caused by Impulse Noise and Steady Noise. Iran J Public Health. 2024;53(7):1537-1548.
