Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification

Heart disease classification is considered a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but achieving higher classification accuracy is a great challenge in the medical sector due to the...

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Main Author:	Mohammed, Alhassan Afnan
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/59562/1/ALHASSAN%20AFNAN%20MOHAMMED%20A%20-%20TESIS%20cut.pdf
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id	my-usm-ep.59562
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spelling	my-usm-ep.595622023-10-30T04:07:37Z Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification 2022-08 Mohammed, Alhassan Afnan QA75.5-76.95 Electronic computers. Computer science Heart disease classification is considered a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but achieving higher classification accuracy is a great challenge in the medical sector due to the presence of noisy, and high-dimensional data. Fuzzy clustering-based filtering methods are introduced for essential feature selection. From the selected features, deep learning has become an important stage for disease diagnosis. However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. Deep Belief Network (DBN) is a sophisticated learning system that requires a high level of approach and executes well. The major contribution of this research is to introduce a Taylor-Bird Swarm optimization-based Deep Belief Network (Taylor-BSA-based DBN) for medical data classification. Firstly, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection and can be utilized for handling high-dimensional data. Then, the selected features are given as input to the DBN classifier which is trained using the Taylor-based bird swarm algorithm (Taylor-BSA). Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). 2022-08 Thesis http://eprints.usm.my/59562/ http://eprints.usm.my/59562/1/ALHASSAN%20AFNAN%20MOHAMMED%20A%20-%20TESIS%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Mohammed, Alhassan Afnan Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
description	Heart disease classification is considered a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but achieving higher classification accuracy is a great challenge in the medical sector due to the presence of noisy, and high-dimensional data. Fuzzy clustering-based filtering methods are introduced for essential feature selection. From the selected features, deep learning has become an important stage for disease diagnosis. However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. Deep Belief Network (DBN) is a sophisticated learning system that requires a high level of approach and executes well. The major contribution of this research is to introduce a Taylor-Bird Swarm optimization-based Deep Belief Network (Taylor-BSA-based DBN) for medical data classification. Firstly, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection and can be utilized for handling high-dimensional data. Then, the selected features are given as input to the DBN classifier which is trained using the Taylor-based bird swarm algorithm (Taylor-BSA). Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohammed, Alhassan Afnan
author_facet	Mohammed, Alhassan Afnan
author_sort	Mohammed, Alhassan Afnan
title	Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
title_short	Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
title_full	Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
title_fullStr	Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
title_full_unstemmed	Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
title_sort	taylor-bird swarm optimization-based deep belief network for medical data classification
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2022
url	http://eprints.usm.my/59562/1/ALHASSAN%20AFNAN%20MOHAMMED%20A%20-%20TESIS%20cut.pdf
_version_	1783729579027857408

Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification

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