Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain

Noise Induced Hearing Loss (NIHL) was the highest reported cases of occupational disease in 2016. Despite the high incidence reported, studies in the method of predictive modelling causes were limited. Hence, this research proposed the development of Artificial Neural Network (ANN) as a tool to iden...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Mohd Zain, Siti Fairus
التنسيق:	أطروحة
اللغة:	English
منشور في:	2019
الموضوعات:	Neural networks (Computer science)
الوصول للمادة أونلاين:	https://ir.uitm.edu.my/id/eprint/84329/1/84329.pdf
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

id	my-uitm-ir.84329
record_format	uketd_dc
spelling	my-uitm-ir.843292024-05-16T01:50:55Z Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain 2019 Mohd Zain, Siti Fairus Neural networks (Computer science) Noise pollution. Noise and its control Noise Induced Hearing Loss (NIHL) was the highest reported cases of occupational disease in 2016. Despite the high incidence reported, studies in the method of predictive modelling causes were limited. Hence, this research proposed the development of Artificial Neural Network (ANN) as a tool to identify and predict risk factors contributed to NIHL. ANN was chosen in this study since it was proven to predict few diseases including coronary heart disease, diabetes, liver cancer and otitis media disease. There are a lot of prediction techniques available in computational models, but this project explored on the Feed Forward Backpropagation Networks as it has been used in predicting complex diseases. This model using a design approach of 24 inputs and 5 binary output layers. The 24 input layers encompassed 12 risk factors and 12 audiogram variables. It also embedded with 10 hidden layers in the prediction models using Levenberg-Marquardt algorithm as a transfer function from input vectors to the five binary outputs. The binary output vectors referred are according to the World Health Organization (WHO) standard, which are classified as either normal, mild, moderate, severe, and profound. The study was focus on examining 355 secondary data extracted from NIHL confirmed cases provided by the Department of Occupational Safety and Health (DOSH), Selangor State. 2019 Thesis https://ir.uitm.edu.my/id/eprint/84329/ https://ir.uitm.edu.my/id/eprint/84329/1/84329.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Sulaiman, Ahmad Asari Yasin, Siti Munira Zamhuri, Mohammad Idris
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sulaiman, Ahmad Asari Yasin, Siti Munira Zamhuri, Mohammad Idris
topic	Neural networks (Computer science) Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Neural networks (Computer science) Mohd Zain, Siti Fairus Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain
description	Noise Induced Hearing Loss (NIHL) was the highest reported cases of occupational disease in 2016. Despite the high incidence reported, studies in the method of predictive modelling causes were limited. Hence, this research proposed the development of Artificial Neural Network (ANN) as a tool to identify and predict risk factors contributed to NIHL. ANN was chosen in this study since it was proven to predict few diseases including coronary heart disease, diabetes, liver cancer and otitis media disease. There are a lot of prediction techniques available in computational models, but this project explored on the Feed Forward Backpropagation Networks as it has been used in predicting complex diseases. This model using a design approach of 24 inputs and 5 binary output layers. The 24 input layers encompassed 12 risk factors and 12 audiogram variables. It also embedded with 10 hidden layers in the prediction models using Levenberg-Marquardt algorithm as a transfer function from input vectors to the five binary outputs. The binary output vectors referred are according to the World Health Organization (WHO) standard, which are classified as either normal, mild, moderate, severe, and profound. The study was focus on examining 355 secondary data extracted from NIHL confirmed cases provided by the Department of Occupational Safety and Health (DOSH), Selangor State.
format	Thesis
qualification_level	Master's degree
author	Mohd Zain, Siti Fairus
author_facet	Mohd Zain, Siti Fairus
author_sort	Mohd Zain, Siti Fairus
title	Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain
title_short	Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain
title_full	Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain
title_fullStr	Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain
title_full_unstemmed	Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain
title_sort	development of noise induced hearing loss prediction model using artificial neural network / siti fairus mohd zain
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/84329/1/84329.pdf
_version_	1804889722315603968

Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain

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