Abstract
This study quantified possible cases of hearing loss and determined possible causes through association rules mining. The data was collected through an online survey that evaluated the medical background of the participants, and a free online hearing test provided by the American company Phonak. The study sample consisted of 226 entries, among students, faculty, and staff members of the Escuela Politécnica Nacional, a public university from Quito, Ecuador. For the initial data treatment and conversion to binary tables of the audiometry results, a Python optical character recognition algorithm was used. Finally, the association rules were obtained using the Apriori algorithm, which was implemented through the arules package for R.
The results showed that 66.36% of the sample presents hearing loss in at least one ear. Furthermore, hearing loss causes in males are primarily related to prolonged use of headphones and loud noises, and these cases are mostly seen in younger individuals; while hearing loss causes in females are primarily related to family history of deafness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
WHO: Addressing the rising prevalence of hearing loss: a WHO report. world health organization, Geneva, Technical report (2018)
WHO: Deafness and hearing loss. https://www.who.int/news-room/fact-sheets. Accessed 09 June 2021
WHO: Global costs of unaddressed hearing loss and cost-effectiveness of interventions: a WHO report. world health organization, Geneva, Technical report (2017)
Key Work Health and Safety Statistics. https://www.safeworkaustralia.gov.au/doc/key-work-health-and-safety-statistics-australia-2020. Accessed 09 June 2021
WHO: Hearing loss due to recreational exposure to loud sounds a WHO review. world health organization, Geneva, Review (2015)
Poushter, J.: Smartphone ownership and internet usage continues to climb in emerging economies, Pew Research Center. Accessed 09 June 2021
Monasta, L.: Burden of disease caused by otitis media: systematic review and global estimates. PLoS One 7(4), e36226 (2012). https://doi.org/10.1371/journal.pone.0036226
Mukherhjea, D.: The design and screening of drugs to prevent acquired sensorineural hearing loss. Expert Opin. Drug Discov. 6(5), 491–505 (2011). https://doi.org/10.1517/17460441.2011.562887
Seddon, J.: Hearing loss in patients on treatment for drug-resistant tuberculosis. Eur. Respir. J. 40, 1277–1286 (2012). https://doi.org/10.1183/09031936.00044812
Consejo Nacional para la Igualdad de Discapacidades Webpage. https://www.consejodiscapacidades.gob.ec/estadisticas-de-discapacidad/. Accessed 09 June 2021
Chaudhuri, A., Mandaviya, K.: Optical character recognition systems for different languages with soft computing. SFSC, vol. 352. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50252-6
Python Software Foundation: pytesseract0.3.7. https://pypi.org/project/pytesseract/. Accessed 09 June 2021
Python Software Foundation: Pillow 8.2.0. https://pypi.org/project/Pillow/. Accessed 09 June 2021
Hahsler, M., et al.: Introduction to arules – a computational environment for mining association rules and frequent item sets. J. Stat. Softw. (2005). https://doi.org/10.18637/jss.v014.i15
Reglas de asociación y algoritmo Apriori con R by Joaquín Amat Rodrigo, available under a Attribution 4.0 International (CC BY 4.0). https://www.cienciadedatos.net/documentos/43_reglas_de_asociacion
Fernández, C., et al.: Extracción de Reglas de Asociación en una Base de Datos Clínicos de Pacientes con Vih/Sida. In: “XXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2006)”, 06 November 2006 - 08 November 2006, Pamplona, España, pp. 136–139 (2006)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record. ACM. (1993). https://doi.org/10.1145/170035.170072
Guan, V.X., Neale, E.P., Tapsell, L.C., Probst, Y.C.: Identifying usual food choice combinations with walnuts: analysis of a 2005–2015 clinical trial cohort of overweight and obese adults. Front. Nutr. 7, 149 (2020). https://doi.org/10.3389/fnut.2020.00149
Acknowledgements
We thank Dr. Eduardo Carrera for providing a guide on the relative deafness scales and the relevant questions for the survey; We also thank Danilo Vásconez for his technical assistance on the elaboration of the figures for the descriptive statistics analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ortiz, R.A., Subía, M.I., Acurio, E., Barba, H. (2022). Hearing Health Virtual Assessment Through Association Rules Mining Inside a College Community. In: Zambrano Vizuete, M., Botto-Tobar, M., Diaz Cadena, A., Durakovic, B. (eds) Innovation and Research - A Driving Force for Socio-Econo-Technological Development. CI3 2021. Lecture Notes in Networks and Systems, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-031-11438-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-11438-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11437-3
Online ISBN: 978-3-031-11438-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)