A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM)

Cardiovascular Diseases, which the Coronary Artery Disease is part of, are the leading cause of death worldwide, despite the progress made in prognosis and treatment. Accurate, noninvasive diagnosis of the disease is impossible to achieve, due to the constrained accuracy of the diagnostic tests and...

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Κύριος συγγραφέας: Αποστολόπουλος, Ιωάννης
Άλλοι συγγραφείς: Apostolopoulos, Ioannis
Γλώσσα:English
Έκδοση: 2021
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Διαθέσιμο Online:http://hdl.handle.net/10889/15083
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spelling nemertes-10889-150832022-09-05T20:52:55Z A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM) Ένα νέο σύστημα υποστήριξης κλινικών αποφάσεων για τη διάγνωση της στεφανιαίας καρδιακής νόσου με τη χρήση ασαφών γνωστικών χαρτών Αποστολόπουλος, Ιωάννης Apostolopoulos, Ioannis Fuzzy cognitive maps Machine learning Deep learning Coronary artery disease Μηχανική μάθηση Στεφανιαία νόσος Ασαφείς γνωστικοί χάρτες Cardiovascular Diseases, which the Coronary Artery Disease is part of, are the leading cause of death worldwide, despite the progress made in prognosis and treatment. Accurate, noninvasive diagnosis of the disease is impossible to achieve, due to the constrained accuracy of the diagnostic tests and the complexity of the parameters affecting the risk of suffering from the disease. Hence, humans are forced to undergo the invasive way of diagnosis and treatment, the Coronary Angiography. It is proven that 30% to 50% of the candidates that undergo the Coronary Angiography were healthy. This is the reason why a lot of research has been going on regarding the prognosis and the automatic diagnosis of CAD. Recently, several approaches have been employed, reclaiming the advances in Data Mining and Machine Learning of the past years. In this work, we approach the problem with Fuzzy Modelling, Machine Learning, and Deep Learning approaches. Based on the Diploma Thesis for the BSc Degree of Electrical and Computer Engineering, an improved method of modelling Coronary Artery Disease with Fuzzy Cognitive Maps is presented in this thesis. Next, Machine Learning and Deep Learning methods are examined. For Machine Learning, state-of the art classifiers are employed to perform the diagnosis. Utilizing the Myocardial Perfusion images from the database, Deep Learning with Convolutional Neural Networks is examined for the classification of the medical images. The state-of-the-art Convolutional Neural Network, called Virtual Geometry Group (VGG) was employed to perform the classification task. The three methods are compared, and conclusions regarding their advantages and drawbacks are discussed. - 2021-07-23T06:51:33Z 2021-07-23T06:51:33Z 2020-07-22 http://hdl.handle.net/10889/15083 en application/pdf
institution UPatras
collection Nemertes
language English
topic Fuzzy cognitive maps
Machine learning
Deep learning
Coronary artery disease
Μηχανική μάθηση
Στεφανιαία νόσος
Ασαφείς γνωστικοί χάρτες
spellingShingle Fuzzy cognitive maps
Machine learning
Deep learning
Coronary artery disease
Μηχανική μάθηση
Στεφανιαία νόσος
Ασαφείς γνωστικοί χάρτες
Αποστολόπουλος, Ιωάννης
A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM)
description Cardiovascular Diseases, which the Coronary Artery Disease is part of, are the leading cause of death worldwide, despite the progress made in prognosis and treatment. Accurate, noninvasive diagnosis of the disease is impossible to achieve, due to the constrained accuracy of the diagnostic tests and the complexity of the parameters affecting the risk of suffering from the disease. Hence, humans are forced to undergo the invasive way of diagnosis and treatment, the Coronary Angiography. It is proven that 30% to 50% of the candidates that undergo the Coronary Angiography were healthy. This is the reason why a lot of research has been going on regarding the prognosis and the automatic diagnosis of CAD. Recently, several approaches have been employed, reclaiming the advances in Data Mining and Machine Learning of the past years. In this work, we approach the problem with Fuzzy Modelling, Machine Learning, and Deep Learning approaches. Based on the Diploma Thesis for the BSc Degree of Electrical and Computer Engineering, an improved method of modelling Coronary Artery Disease with Fuzzy Cognitive Maps is presented in this thesis. Next, Machine Learning and Deep Learning methods are examined. For Machine Learning, state-of the art classifiers are employed to perform the diagnosis. Utilizing the Myocardial Perfusion images from the database, Deep Learning with Convolutional Neural Networks is examined for the classification of the medical images. The state-of-the-art Convolutional Neural Network, called Virtual Geometry Group (VGG) was employed to perform the classification task. The three methods are compared, and conclusions regarding their advantages and drawbacks are discussed.
author2 Apostolopoulos, Ioannis
author_facet Apostolopoulos, Ioannis
Αποστολόπουλος, Ιωάννης
author Αποστολόπουλος, Ιωάννης
author_sort Αποστολόπουλος, Ιωάννης
title A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM)
title_short A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM)
title_full A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM)
title_fullStr A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM)
title_full_unstemmed A new medical decision support system (MDSS) for the diagnosis of coronary artery disease (CAD) using fuzzy cognitive maps (FCM)
title_sort new medical decision support system (mdss) for the diagnosis of coronary artery disease (cad) using fuzzy cognitive maps (fcm)
publishDate 2021
url http://hdl.handle.net/10889/15083
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