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

Introduction: With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary. Objective: This study was designed aimed to...

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Autores principales:	Sajad Zare, Mohammad Reza Ghotbi-Ravandi, Hossein ElahiShirvan, Mostafa Ghazizadeh Ahsaee, Mina Rostami
Formato:	article
Lenguaje:	EN
Publicado:	Ubiquity Press 2019
Materias:	Infectious and parasitic diseases RC109-216 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/ef37716a6816499ca29eef7874461b71
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spelling	oai:doaj.org-article:ef37716a6816499ca29eef7874461b712021-12-02T06:08:59ZPredicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm2214-999610.5334/aogh.2522https://doaj.org/article/ef37716a6816499ca29eef7874461b712019-06-01T00:00:00Zhttps://annalsofglobalhealth.org/articles/2522https://doaj.org/toc/2214-9996Introduction: With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary. Objective: This study was designed aimed to use the C5 algorithm to determine the weight of factors affecting the workers’ hearing loss based on the audiometric data. Methods: This cross-sectional, descriptive, analytical study was conducted in 2018 in a mining industry in southeastern Iran. In this study, workers were divided into three exposed groups with different sound pressure levels (one control group and two case groups). Audiometry was conducted for each group of 50 persons; hence, the total number of subjects was 150. The stages of this study include: 1) selecting factors (predictive) to check and weigh them; 2) conducting the audiometry for both ears; 3) calculating the permanent hearing loss in each ear and permanent hearing loss of both ears; 4) classifying the types of hearing loss; and 5) investigating and determining the weight of factors affecting the hearing loss and their classification based on the C5 algorithm and determining the error and accuracy rate of each model. To assess and determine the factors affecting the hearing loss of workers, the C5 algorithm and IBM SPSS Modeler 18.0 were used. SPSS V.18 was used to analyze the linear regression and paired t-test tests, too. Results: The results showed that in the first model (SPL <70 dBA), the 8KHz frequency with the weight of 31% had the highest effect, the factors of work experience and the frequency of 250Hz each with the weight of 3%, had the least effect, and the accuracy of the model was 100%. In the second model (SPL 70–80 dBA) the frequency of 8KHz with the weight of 21% had the highest effect, the frequency of 250Hz and the working experience each had the lowest effect with the weight of 7% and the accuracy of the model was calculated as 100%. In the third model (SPL >85 dBA), the 4KHz frequency with the weight of 31% had the highest effect, and the work experience with a weight of 1% had the lowest effect, and the accuracy of the model was 94%. In the fourth model, the 4KHz frequency with the weight of 22% had the highest effect and 250Hz and age each with the weight of 8% had the lowest effects; the accuracy of this model was calculated to be 99.05%. Conclusions: During investigating and determining the weight of the factors affecting hearing loss by the C5 algorithm, the high weight and effect of the 4KHz frequency were predicted in hearing loss changes. Considering the high accuracy obtained in this modeling, this algorithm is a suitable and powerful tool for predicting and modeling the hearing loss.Sajad ZareMohammad Reza Ghotbi-RavandiHossein ElahiShirvanMostafa Ghazizadeh AhsaeeMina RostamiUbiquity PressarticleInfectious and parasitic diseasesRC109-216Public aspects of medicineRA1-1270ENAnnals of Global Health, Vol 85, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Infectious and parasitic diseases RC109-216 Public aspects of medicine RA1-1270
spellingShingle	Infectious and parasitic diseases RC109-216 Public aspects of medicine RA1-1270 Sajad Zare Mohammad Reza Ghotbi-Ravandi Hossein ElahiShirvan Mostafa Ghazizadeh Ahsaee Mina Rostami Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm
description	Introduction: With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary. Objective: This study was designed aimed to use the C5 algorithm to determine the weight of factors affecting the workers’ hearing loss based on the audiometric data. Methods: This cross-sectional, descriptive, analytical study was conducted in 2018 in a mining industry in southeastern Iran. In this study, workers were divided into three exposed groups with different sound pressure levels (one control group and two case groups). Audiometry was conducted for each group of 50 persons; hence, the total number of subjects was 150. The stages of this study include: 1) selecting factors (predictive) to check and weigh them; 2) conducting the audiometry for both ears; 3) calculating the permanent hearing loss in each ear and permanent hearing loss of both ears; 4) classifying the types of hearing loss; and 5) investigating and determining the weight of factors affecting the hearing loss and their classification based on the C5 algorithm and determining the error and accuracy rate of each model. To assess and determine the factors affecting the hearing loss of workers, the C5 algorithm and IBM SPSS Modeler 18.0 were used. SPSS V.18 was used to analyze the linear regression and paired t-test tests, too. Results: The results showed that in the first model (SPL <70 dBA), the 8KHz frequency with the weight of 31% had the highest effect, the factors of work experience and the frequency of 250Hz each with the weight of 3%, had the least effect, and the accuracy of the model was 100%. In the second model (SPL 70–80 dBA) the frequency of 8KHz with the weight of 21% had the highest effect, the frequency of 250Hz and the working experience each had the lowest effect with the weight of 7% and the accuracy of the model was calculated as 100%. In the third model (SPL >85 dBA), the 4KHz frequency with the weight of 31% had the highest effect, and the work experience with a weight of 1% had the lowest effect, and the accuracy of the model was 94%. In the fourth model, the 4KHz frequency with the weight of 22% had the highest effect and 250Hz and age each with the weight of 8% had the lowest effects; the accuracy of this model was calculated to be 99.05%. Conclusions: During investigating and determining the weight of the factors affecting hearing loss by the C5 algorithm, the high weight and effect of the 4KHz frequency were predicted in hearing loss changes. Considering the high accuracy obtained in this modeling, this algorithm is a suitable and powerful tool for predicting and modeling the hearing loss.
format	article
author	Sajad Zare Mohammad Reza Ghotbi-Ravandi Hossein ElahiShirvan Mostafa Ghazizadeh Ahsaee Mina Rostami
author_facet	Sajad Zare Mohammad Reza Ghotbi-Ravandi Hossein ElahiShirvan Mostafa Ghazizadeh Ahsaee Mina Rostami
author_sort	Sajad Zare
title	Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm
title_short	Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm
title_full	Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm
title_fullStr	Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm
title_full_unstemmed	Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm
title_sort	predicting and weighting the factors affecting workers’ hearing loss based on audiometric data using c5 algorithm
publisher	Ubiquity Press
publishDate	2019
url	https://doaj.org/article/ef37716a6816499ca29eef7874461b71
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Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm

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