Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach

Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can b...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Electronics (Basel) 2022-06, Vol.11 (12), p.1890
Hauptverfasser: Bangyal, Waqas Haider, Rehman, Najeeb Ur, Nawaz, Asma, Nisar, Kashif, Ibrahim, Ag. Asri Ag, Shakir, Rabia, Rawat, Danda B.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 12
container_start_page 1890
container_title Electronics (Basel)
container_volume 11
creator Bangyal, Waqas Haider
Rehman, Najeeb Ur
Nawaz, Asma
Nisar, Kashif
Ibrahim, Ag. Asri Ag
Shakir, Rabia
Rawat, Danda B.
description Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can be helpful for better treatment and the prevention of brain tissue destruction. Researchers have used machine learning techniques to predict the early detection of AD. However, Alzheimer’s disorders are still underexplored in the knowledge domain. In the biomedical field, the illustration of terminologies and notions is essential. Multiple methods are adopted to represent these notions, but ontologies are the most frequent and accurate. Ontology construction is a complex and time-consuming process. The designed ontology relies on Disease Ontology (DO), which is considered the benchmark in medical practice. Ontology reasoning mechanisms can be adopted for AD identification. In this paper, a deep convolutional neural network-based approach is proposed to diagnose Alzheimer’s disease, using an AD dataset acquired from Kaggle. Machine learning-based approaches (logistic regression, gradient boosting, XGB, SGD, MLP, SVM, KNN, random forest) are also used for a fair comparison. The simulation results are generated using three strategies (default parameters, 10-cross validation, and grid search), and MLP provides superior results on a default parameter strategy with an accuracy of 92.12%. Furthermore, the deep learning-based approach convolutional neural network (CNN) achieved an accuracy of 94.61%. The experimental results indicate that the construction of ontology, with the help of deep learning knowledge, can produce better results where the robustness and scalability can be enhanced. In comparisons to other methods, the CNN results are excellent and encouraging.
doi_str_mv 10.3390/electronics11121890
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2679711923</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2679711923</sourcerecordid><originalsourceid>FETCH-LOGICAL-c322t-a9cd3c83d1249eabce51f57169d3a50fca68209e9563df347b7c872321e7c953</originalsourceid><addsrcrecordid>eNptkD9vwjAQxa2qlYoon6CLpc5pbR-JcyOF_pMiscAcGecCQcFO7TDQT99QOnToLe9O99N70mPsXopHABRP1JLtg3eNjVJKJXMUV2ykhMYEFarrP_stm8S4F8OghBzEiK3n3sU-HG3fuC1f-INpHF-63rd-e-K1D3zWfu2oOVDgiyaSicTX8Ycl6nhBJrjz9Tw8Kj7ruuCN3d2xm9q0kSa_Omar15fV_D0plm8f81mRWFCqTwzaCmwOlVRTJLOxlMo61TLDCkwqamuyXAkkTDOoapjqjba5VqAkaYspjNnDxXZI_TxS7Mu9PwY3JJYq06ilRAUDBRfKBh9joLrsQnMw4VRKUZ4bLP9pEL4B-QlnEw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2679711923</pqid></control><display><type>article</type><title>Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Bangyal, Waqas Haider ; Rehman, Najeeb Ur ; Nawaz, Asma ; Nisar, Kashif ; Ibrahim, Ag. Asri Ag ; Shakir, Rabia ; Rawat, Danda B.</creator><creatorcontrib>Bangyal, Waqas Haider ; Rehman, Najeeb Ur ; Nawaz, Asma ; Nisar, Kashif ; Ibrahim, Ag. Asri Ag ; Shakir, Rabia ; Rawat, Danda B.</creatorcontrib><description>Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can be helpful for better treatment and the prevention of brain tissue destruction. Researchers have used machine learning techniques to predict the early detection of AD. However, Alzheimer’s disorders are still underexplored in the knowledge domain. In the biomedical field, the illustration of terminologies and notions is essential. Multiple methods are adopted to represent these notions, but ontologies are the most frequent and accurate. Ontology construction is a complex and time-consuming process. The designed ontology relies on Disease Ontology (DO), which is considered the benchmark in medical practice. Ontology reasoning mechanisms can be adopted for AD identification. In this paper, a deep convolutional neural network-based approach is proposed to diagnose Alzheimer’s disease, using an AD dataset acquired from Kaggle. Machine learning-based approaches (logistic regression, gradient boosting, XGB, SGD, MLP, SVM, KNN, random forest) are also used for a fair comparison. The simulation results are generated using three strategies (default parameters, 10-cross validation, and grid search), and MLP provides superior results on a default parameter strategy with an accuracy of 92.12%. Furthermore, the deep learning-based approach convolutional neural network (CNN) achieved an accuracy of 94.61%. The experimental results indicate that the construction of ontology, with the help of deep learning knowledge, can produce better results where the robustness and scalability can be enhanced. In comparisons to other methods, the CNN results are excellent and encouraging.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics11121890</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Alzheimer's disease ; Artificial neural networks ; Brain ; Deep learning ; Domains ; Knowledge representation ; Machine learning ; Magnetic resonance imaging ; Neural networks ; Ontology ; Parameters</subject><ispartof>Electronics (Basel), 2022-06, Vol.11 (12), p.1890</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c322t-a9cd3c83d1249eabce51f57169d3a50fca68209e9563df347b7c872321e7c953</citedby><cites>FETCH-LOGICAL-c322t-a9cd3c83d1249eabce51f57169d3a50fca68209e9563df347b7c872321e7c953</cites><orcidid>0000-0003-1793-5905 ; 0000-0003-3638-3464 ; 0000-0003-1662-2069 ; 0000-0001-8092-4665 ; 0000-0002-5797-4821</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Bangyal, Waqas Haider</creatorcontrib><creatorcontrib>Rehman, Najeeb Ur</creatorcontrib><creatorcontrib>Nawaz, Asma</creatorcontrib><creatorcontrib>Nisar, Kashif</creatorcontrib><creatorcontrib>Ibrahim, Ag. Asri Ag</creatorcontrib><creatorcontrib>Shakir, Rabia</creatorcontrib><creatorcontrib>Rawat, Danda B.</creatorcontrib><title>Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach</title><title>Electronics (Basel)</title><description>Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can be helpful for better treatment and the prevention of brain tissue destruction. Researchers have used machine learning techniques to predict the early detection of AD. However, Alzheimer’s disorders are still underexplored in the knowledge domain. In the biomedical field, the illustration of terminologies and notions is essential. Multiple methods are adopted to represent these notions, but ontologies are the most frequent and accurate. Ontology construction is a complex and time-consuming process. The designed ontology relies on Disease Ontology (DO), which is considered the benchmark in medical practice. Ontology reasoning mechanisms can be adopted for AD identification. In this paper, a deep convolutional neural network-based approach is proposed to diagnose Alzheimer’s disease, using an AD dataset acquired from Kaggle. Machine learning-based approaches (logistic regression, gradient boosting, XGB, SGD, MLP, SVM, KNN, random forest) are also used for a fair comparison. The simulation results are generated using three strategies (default parameters, 10-cross validation, and grid search), and MLP provides superior results on a default parameter strategy with an accuracy of 92.12%. Furthermore, the deep learning-based approach convolutional neural network (CNN) achieved an accuracy of 94.61%. The experimental results indicate that the construction of ontology, with the help of deep learning knowledge, can produce better results where the robustness and scalability can be enhanced. In comparisons to other methods, the CNN results are excellent and encouraging.</description><subject>Alzheimer's disease</subject><subject>Artificial neural networks</subject><subject>Brain</subject><subject>Deep learning</subject><subject>Domains</subject><subject>Knowledge representation</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Neural networks</subject><subject>Ontology</subject><subject>Parameters</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNptkD9vwjAQxa2qlYoon6CLpc5pbR-JcyOF_pMiscAcGecCQcFO7TDQT99QOnToLe9O99N70mPsXopHABRP1JLtg3eNjVJKJXMUV2ykhMYEFarrP_stm8S4F8OghBzEiK3n3sU-HG3fuC1f-INpHF-63rd-e-K1D3zWfu2oOVDgiyaSicTX8Ycl6nhBJrjz9Tw8Kj7ruuCN3d2xm9q0kSa_Omar15fV_D0plm8f81mRWFCqTwzaCmwOlVRTJLOxlMo61TLDCkwqamuyXAkkTDOoapjqjba5VqAkaYspjNnDxXZI_TxS7Mu9PwY3JJYq06ilRAUDBRfKBh9joLrsQnMw4VRKUZ4bLP9pEL4B-QlnEw</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Bangyal, Waqas Haider</creator><creator>Rehman, Najeeb Ur</creator><creator>Nawaz, Asma</creator><creator>Nisar, Kashif</creator><creator>Ibrahim, Ag. Asri Ag</creator><creator>Shakir, Rabia</creator><creator>Rawat, Danda B.</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-1793-5905</orcidid><orcidid>https://orcid.org/0000-0003-3638-3464</orcidid><orcidid>https://orcid.org/0000-0003-1662-2069</orcidid><orcidid>https://orcid.org/0000-0001-8092-4665</orcidid><orcidid>https://orcid.org/0000-0002-5797-4821</orcidid></search><sort><creationdate>20220601</creationdate><title>Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach</title><author>Bangyal, Waqas Haider ; Rehman, Najeeb Ur ; Nawaz, Asma ; Nisar, Kashif ; Ibrahim, Ag. Asri Ag ; Shakir, Rabia ; Rawat, Danda B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c322t-a9cd3c83d1249eabce51f57169d3a50fca68209e9563df347b7c872321e7c953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Alzheimer's disease</topic><topic>Artificial neural networks</topic><topic>Brain</topic><topic>Deep learning</topic><topic>Domains</topic><topic>Knowledge representation</topic><topic>Machine learning</topic><topic>Magnetic resonance imaging</topic><topic>Neural networks</topic><topic>Ontology</topic><topic>Parameters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bangyal, Waqas Haider</creatorcontrib><creatorcontrib>Rehman, Najeeb Ur</creatorcontrib><creatorcontrib>Nawaz, Asma</creatorcontrib><creatorcontrib>Nisar, Kashif</creatorcontrib><creatorcontrib>Ibrahim, Ag. Asri Ag</creatorcontrib><creatorcontrib>Shakir, Rabia</creatorcontrib><creatorcontrib>Rawat, Danda B.</creatorcontrib><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bangyal, Waqas Haider</au><au>Rehman, Najeeb Ur</au><au>Nawaz, Asma</au><au>Nisar, Kashif</au><au>Ibrahim, Ag. Asri Ag</au><au>Shakir, Rabia</au><au>Rawat, Danda B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach</atitle><jtitle>Electronics (Basel)</jtitle><date>2022-06-01</date><risdate>2022</risdate><volume>11</volume><issue>12</issue><spage>1890</spage><pages>1890-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can be helpful for better treatment and the prevention of brain tissue destruction. Researchers have used machine learning techniques to predict the early detection of AD. However, Alzheimer’s disorders are still underexplored in the knowledge domain. In the biomedical field, the illustration of terminologies and notions is essential. Multiple methods are adopted to represent these notions, but ontologies are the most frequent and accurate. Ontology construction is a complex and time-consuming process. The designed ontology relies on Disease Ontology (DO), which is considered the benchmark in medical practice. Ontology reasoning mechanisms can be adopted for AD identification. In this paper, a deep convolutional neural network-based approach is proposed to diagnose Alzheimer’s disease, using an AD dataset acquired from Kaggle. Machine learning-based approaches (logistic regression, gradient boosting, XGB, SGD, MLP, SVM, KNN, random forest) are also used for a fair comparison. The simulation results are generated using three strategies (default parameters, 10-cross validation, and grid search), and MLP provides superior results on a default parameter strategy with an accuracy of 92.12%. Furthermore, the deep learning-based approach convolutional neural network (CNN) achieved an accuracy of 94.61%. The experimental results indicate that the construction of ontology, with the help of deep learning knowledge, can produce better results where the robustness and scalability can be enhanced. In comparisons to other methods, the CNN results are excellent and encouraging.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics11121890</doi><orcidid>https://orcid.org/0000-0003-1793-5905</orcidid><orcidid>https://orcid.org/0000-0003-3638-3464</orcidid><orcidid>https://orcid.org/0000-0003-1662-2069</orcidid><orcidid>https://orcid.org/0000-0001-8092-4665</orcidid><orcidid>https://orcid.org/0000-0002-5797-4821</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2079-9292
ispartof Electronics (Basel), 2022-06, Vol.11 (12), p.1890
issn 2079-9292
2079-9292
language eng
recordid cdi_proquest_journals_2679711923
source MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals
subjects Alzheimer's disease
Artificial neural networks
Brain
Deep learning
Domains
Knowledge representation
Machine learning
Magnetic resonance imaging
Neural networks
Ontology
Parameters
title Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T17%3A55%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Constructing%20Domain%20Ontology%20for%20Alzheimer%20Disease%20Using%20Deep%20Learning%20Based%20Approach&rft.jtitle=Electronics%20(Basel)&rft.au=Bangyal,%20Waqas%20Haider&rft.date=2022-06-01&rft.volume=11&rft.issue=12&rft.spage=1890&rft.pages=1890-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics11121890&rft_dat=%3Cproquest_cross%3E2679711923%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2679711923&rft_id=info:pmid/&rfr_iscdi=true