Heart Failure diagnosis based on deep learning techniques
The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs) are used to pre-train Deep Belief Networks (DBN) a...
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| Published in: | 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 1757 - 1760 |
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| Main Authors: | , , , , , , , , , , , , |
| Format: | Conference Proceeding Journal Article |
| Language: | English |
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United States
IEEE
01.11.2021
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| ISSN: | 2694-0604, 2694-0604 |
| Online Access: | Get full text |
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| Abstract | The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs) are used to pre-train Deep Belief Networks (DBN) and Deep Neural Networks (DNN). The data is provided by the University College Dublin and the 2nd Department of Cardiology from the University Hospital of Ioannina. The features recorded are grouped into: general demographic information, physical examination, classical cardiovascular risk factors, personal history of cardiovascular disease, symptoms, medications, echocardiographic features, laboratory findings, lifestyle/habits and other diseases. The total number of subjects utilized is 422. The deep learning methods provide quite high results with the Autoencoder plus DNN approach to demonstrate accuracy 91.71%, sensitivity 90.74%, specificity 92.31% and f-score 89.36%. |
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| AbstractList | The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs) are used to pre-train Deep Belief Networks (DBN) and Deep Neural Networks (DNN). The data is provided by the University College Dublin and the 2nd Department of Cardiology from the University Hospital of Ioannina. The features recorded are grouped into: general demographic information, physical examination, classical cardiovascular risk factors, personal history of cardiovascular disease, symptoms, medications, echocardiographic features, laboratory findings, lifestyle/habits and other diseases. The total number of subjects utilized is 422. The deep learning methods provide quite high results with the Autoencoder plus DNN approach to demonstrate accuracy 91.71%, sensitivity 90.74%, specificity 92.31% and f-score 89.36%. The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs) are used to pre-train Deep Belief Networks (DBN) and Deep Neural Networks (DNN). The data is provided by the University College Dublin and the 2nd Department of Cardiology from the University Hospital of Ioannina. The features recorded are grouped into: general demographic information, physical examination, classical cardiovascular risk factors, personal history of cardiovascular disease, symptoms, medications, echocardiographic features, laboratory findings, lifestyle/habits and other diseases. The total number of subjects utilized is 422. The deep learning methods provide quite high results with the Autoencoder plus DNN approach to demonstrate accuracy 91.71%, sensitivity 90.74%, specificity 92.31% and f-score 89.36%.The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs) are used to pre-train Deep Belief Networks (DBN) and Deep Neural Networks (DNN). The data is provided by the University College Dublin and the 2nd Department of Cardiology from the University Hospital of Ioannina. The features recorded are grouped into: general demographic information, physical examination, classical cardiovascular risk factors, personal history of cardiovascular disease, symptoms, medications, echocardiographic features, laboratory findings, lifestyle/habits and other diseases. The total number of subjects utilized is 422. The deep learning methods provide quite high results with the Autoencoder plus DNN approach to demonstrate accuracy 91.71%, sensitivity 90.74%, specificity 92.31% and f-score 89.36%. |
| Author | Ledwidge, Mark Gallagher, Joseph Plati, Daphni Watson, Chris McDonald, Kenneth Naka, Katerina K. Goletsis, Yorgos Fotiadis, Dimitrios I. Pharithi, Rebabonye Rammos, Aidonis Tripoliti, Evanthia E. Papadopoulos, Theofilos G. Bechlioulis, Aris |
| Author_xml | – sequence: 1 givenname: Theofilos G. surname: Papadopoulos fullname: Papadopoulos, Theofilos G. email: tpapado2011@gmail.com organization: University of Ioannina,Unit of Medical Technology and Intelligent Information Systems,Ioannina,Greece – sequence: 2 givenname: Daphni surname: Plati fullname: Plati, Daphni email: daphni.plati@gmail.com organization: Institute of Molecular Biology and Biotechnology, FORTH,Department of Biomedical Research,Ioannina,Greece – sequence: 3 givenname: Evanthia E. surname: Tripoliti fullname: Tripoliti, Evanthia E. email: etripoliti@gmail.com organization: Institute of Molecular Biology and Biotechnology, FORTH,Department of Biomedical Research,Ioannina,Greece – sequence: 4 givenname: Yorgos surname: Goletsis fullname: Goletsis, Yorgos email: goletsis@uoi.gr organization: University of Ioannina,Department of Economics,Ioannina,Greece – sequence: 5 givenname: Katerina K. surname: Naka fullname: Naka, Katerina K. email: drkknaka@gmail.com organization: University of Ioannina,Medical School,2nd Department of Cardiology,Ioannina,Greece – sequence: 6 givenname: Aidonis surname: Rammos fullname: Rammos, Aidonis email: aidrammos@yahoo.gr organization: University of Ioannina,Medical School,2nd Department of Cardiology,Ioannina,Greece – sequence: 7 givenname: Aris surname: Bechlioulis fullname: Bechlioulis, Aris email: md02798@yahoo.gr organization: University of Ioannina,Medical School,2nd Department of Cardiology,Ioannina,Greece – sequence: 8 givenname: Chris surname: Watson fullname: Watson, Chris email: chris.watson@qub.ac.uk organization: National University of Ireland,University College Dublin,Belfield,Dublin,Ireland – sequence: 9 givenname: Kenneth surname: McDonald fullname: McDonald, Kenneth email: kenneth.mcdonald@ucd.ie organization: National University of Ireland,University College Dublin,Belfield,Dublin,Ireland – sequence: 10 givenname: Mark surname: Ledwidge fullname: Ledwidge, Mark email: mark.ledwidge@ucd.ie organization: National University of Ireland,University College Dublin,Belfield,Dublin,Ireland – sequence: 11 givenname: Rebabonye surname: Pharithi fullname: Pharithi, Rebabonye email: rpharithi@gmail.com organization: National University of Ireland,University College Dublin,Belfield,Dublin,Ireland – sequence: 12 givenname: Joseph surname: Gallagher fullname: Gallagher, Joseph email: jgallagher@ucd.ie organization: National University of Ireland,University College Dublin,Belfield,Dublin,Ireland – sequence: 13 givenname: Dimitrios I. surname: Fotiadis fullname: Fotiadis, Dimitrios I. email: fotiadis@cc.uoi.gr organization: University of Ioannina,Unit of Medical Technology and Intelligent Information Systems,Ioannina,Greece |
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| SubjectTerms | Algorithms Biological system modeling Deep Learning Electrocardiography Heart Heart Failure - diagnosis History Hospitals Humans Neural Networks, Computer Sensitivity |
| Title | Heart Failure diagnosis based on deep learning techniques |
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