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Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm Cover

Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm

Open Access
|Jun 2019

References

  1. Zare S, Nassiri P, Monazzam MR, Pourbakht A, Azam K and Golmohammadi T. Evaluation of the effects of occupational noise exposure on serum aldosterone and potassium among industrial workers. Noise and Health. 2016; 18(80): 1. DOI: 10.4103/1463-1741.174358
  2. Nassiri P, Zare S, Monazzam MR, Pourbakht A, Azam K and Golmohammadi T. Evaluation of the effects of various sound pressure levels on the level of serum aldosterone concentration in rats. Noise and Health. 2017; 19(89): 200. DOI: 10.4103/nah.NAH_64_16
  3. Zare S, Hasheminejad N, Shirvan HE, Hasanvand D, Hemmatjo R and Ahmadi S. Assessing individual and environmental sound pressure level and sound mapping in Iranian safety shoes factory. Rom J Acoust Vib. 2018; 15(1): 205.
  4. Nassiri P, Zare S, Monazzam MR, Pourbakht A, Azam K and Golmohammadi T. Modeling signal-to-noise ratio of otoacoustic emissions in workers exposed to different industrial noise levels. Noise and Health. 2016; 18(85): 391. DOI: 10.4103/1463-1741.174358
  5. Zamanian Z, Rostami R, Hasanzadeh J and Hashemi H. Investigation of the effect of occupational noise exposure on blood pressure and heart rate of steel industry workers. J Environ Public Health; 2013. DOI: 10.1155/2013/256060
  6. Kazemi R, Motamedzade M, Golmohammadi R, Mokarami H, Hemmatjo R and Heidarimoghadam R. Field study of effects of night shifts on cognitive performance, salivary melatonin, and sleep. Saf Health Work. 2018; 9(2): 2039. DOI: 10.1016/j.shaw.2017.07.007
  7. Zare S, Hemmatjo R, Allahyari T, et al. Comparison of the effect of typical firefighting activities, live fire drills and rescue operations at height on firefighters’ physiological responses and cognitive function. Ergonomics. 2018; 61(10): 133444. DOI: 10.1080/00140139.2018.1484524
  8. Safari Variani A, Ahmadi S, Zare S and Ghorbanideh M. Water pump noise control using designed acoustic curtains in a residential building of Qazvin city. Iran Occup Health. 2018; 15(1): 12635.
  9. Han MA, Back SA, Kim HL, Park SY, Yeo SW and Park SN. Therapeutic effect of dexamethasone for noise-induced hearing loss: Systemic versus intratympanic injection in mice. Otol Neurotol. 2015; 36(5): 75562. DOI: 10.1097/MAO.0000000000000759
  10. Jenkins KA, Fodor C, Presacco A and Anderson S. Effects of amplification on neural phase locking, amplitude, and latency to a speech syllable. Ear Hear. 2018; 39(4): 81024. DOI: 10.1097/AUD.0000000000000538
  11. Sliwinska-Kowalska M and Pawelczyk M. Contribution of genetic factors to noise-induced hearing loss: A human studies review. Mutat Res Mutat Res. 2013; 752(1): 615. DOI: 10.1016/j.mrrev.2012.11.001
  12. Vaisbuch Y, Alyono JC, Kandathil C, Wu SH, Fitzgerald MB and Jackler RK. Occupational noise exposure and risk for noise-induced hearing loss due to temporal bone drilling. Otol Neurotol. 2018; 39(6): 6939. DOI: 10.1097/MAO.0000000000001851
  13. Borchgrevink HM. Does health promotion work in relation to noise? Noise Health. 2003; 5(18): 25.
  14. Dobie RA. Cost-effective hearing conservation: Regulatory and research priorities. Ear Hear. 2018; 39(4): 62130. DOI: 10.1097/AUD.0000000000000523
  15. Chordekar S, Perez R, Adelman C, Sohmer H and Kishon-Rabin L. Does hearing in response to soft-tissue stimulation involve skull vibrations? A within-subject comparison between skull vibration magnitudes and hearing thresholds. Hear Res. 2018; 364: 5967. DOI: 10.1016/j.heares.2018.03.030
  16. Guerra-Jiménez G, De Miguel ÁR, González JCF, Barreiro SAB, Plasencia DP and Macías ÁR. Cochlear implant evaluation: Prognosis estimation by data mining system. J Int Adv Otol. 2016; 12(1): 1. DOI: 10.5152/iao.2016.510
  17. Montgomery A. Data mining—business hunching, not just data Crunching. In: Proc of the Second International Conference on the Application of Knowledge Discovery and Data Mining PADD. 1998; 3948.
  18. won Lee J and Giraud-Carrier C. Results on mining NHANES data: A case study in evidence-based medicine. Comput Biol Med. 2013; 43(5): 493503. DOI: 10.1016/j.compbiomed.2013.02.018
  19. Pandya R and Pandya J. C5.0 algorithm to improved decision tree with feature selection and reduced error pruning. Int J Comput Appl. 2015; 117(16). DOI: 10.5120/20639-3318
  20. Majumder J and Sharma LK. Application of data mining techniques to audiometric data among professionals in India. J Sci Res Reports. 2014; 3(23): 2860971. DOI: 10.9734/JSRR/2014/12700
  21. Ramos-Miguel A, Perez-Zaballos T, Perez D, Falconb JC and Ramosb A. Use of data mining to predict significant factors and benefits of bilateral cochlear implantation. Eur Arch Oto-Rhino-Laryngology. 2015; 272(11): 315762. DOI: 10.1007/s00405-014-3337-3
  22. Nawi NM, Rehman MZ and Ghazali MI. Noise-induced hearing loss prediction in Malaysian industrial workers using gradient descent with adaptive momentum algorithm. Int Rev Comput Softw. 2011; 6(5): 7408.
  23. Golmohammadi R and Aliabadi M. Noise and vibration engineering. Daneshju; 1999.
  24. Schlauch RS and Nelson P. Puretone evaluation. Handb Clin Audiol. 2009; 6: 3049.
  25. Gubbels SP, Gartrell BC, Ploch JL and Hanson KD. Can routine office-based audiometry predict cochlear implant evaluation results? Laryngoscope. 2017; 127(1): 21622. DOI: 10.1002/lary.26066
  26. World Health Organization (WHO). Report of the Informal Working Group on Prevention of Deafness and Hearing Impairment Programme Planning, Geneva. 1991; 1821.
  27. Han J, Pei J and Kamber M. Data mining: Concepts and techniques. Elsevier. 2011.
  28. Marcoulides GA. Discovering knowledge in data: An Introduction to data mining. Taylor & Francis; 2005. DOI: 10.1198/jasa.2005.s61
  29. Aghilinezhad MA, Ali MI, Mohammadi S and Falahi M. Assessment of the effect of occupational noise on workers hearing in small scale industries in Tehran; 2007.
  30. Dehghani M, Pourjabbari AA and Ravandi MR. Relationship between noise pollution and hearing loss among workers in Sarkhoon Gas Refinery. Bimon J Hormozgan Univ Med Sci. 2012; 16(3): 1818.
  31. Exarchos TP, Rigas G, Bibas A, et al. Mining balance disorders’ data for the development of diagnostic decision support systems. Comput Biol Med. 2016; 77: 2408. DOI: 10.1016/j.compbiomed.2016.08.016
  32. Seixas FL, Zadrozny B, Laks J, Conci A and Saade DCM. A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer’s disease and mild cognitive impairment. Comput Biol Med. 2014; 51: 14058. DOI: 10.1016/j.compbiomed.2014.04.010
  33. Miettinen K and Juhola M. Classification of otoneurological cases according to Bayesian probabilistic models. J Med Syst. 2010; 34(2): 11930. DOI: 10.1007/s10916-008-9223-z
  34. Acır N, Özdamar Ö, Güzeliş C. Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection. Eng Appl Artif Intell. 2006; 19(2): 20918. DOI: 10.1016/j.engappai.2005.08.004
DOI: https://doi.org/10.5334/aogh.2522 | Journal eISSN: 2214-9996
Language: English
Published on: Jun 18, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Sajad Zare, Mohammad Reza Ghotbi-Ravandi, Hossein ElahiShirvan, Mostafa Ghazizadeh Ahsaee, Mina Rostami, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.