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A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network

Cardiovascular disease is the leading cause of death, and specialists estimate that roughly half of all heart attacks and strokes happen in individuals who have not been flagged as 'at risk. Hence, there's an urgent need to progress the exactness of heart infection diagnosis. To this end,...

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Bibliographic Details
Main Author: Arulmolly, Annathurai
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak (UNIMAS) 2019
Subjects:
BF Psychology
R Medicine (General)
Online Access:http://ir.unimas.my/id/eprint/27551/1/A%20deep%20learning%20approach%20for%20heart%20disease%20classification%20using...%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/27551/8/Arulmolly%20Annathurai%20ft.pdf
http://ir.unimas.my/id/eprint/27551/
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spelling my.unimas.ir-275512024-11-14T02:28:31Z http://ir.unimas.my/id/eprint/27551/ A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network Arulmolly, Annathurai BF Psychology R Medicine (General) Cardiovascular disease is the leading cause of death, and specialists estimate that roughly half of all heart attacks and strokes happen in individuals who have not been flagged as 'at risk. Hence, there's an urgent need to progress the exactness of heart infection diagnosis. To this end, we explore the potential of utilizing information examination, and in specific the plan and utilize of convolutional neural networks (CNN) for classify heart disease based on ultrasound heart images. Our fundamental commitment is the plan, assessment, and optimization of CNN models of expanding profundity for heart disease classification. Moreover, a system with high precision and accuracy is required to analyse heart disease classification. This study utilized ultrasound scanned heart images which are collected from local hospital. A total of 50 images which includes 20 normal and 30 abnormal heart images. The model has been run successfully without any errors and produce a high accuracy of 96% after running for 46 epochs. Furthermore, a comparative study has been done between SVM and CNN using the similar dataset to analyse performance based on the accuracy. Universiti Malaysia Sarawak (UNIMAS) 2019 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/27551/1/A%20deep%20learning%20approach%20for%20heart%20disease%20classification%20using...%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/27551/8/Arulmolly%20Annathurai%20ft.pdf Arulmolly, Annathurai (2019) A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic BF Psychology
R Medicine (General)
spellingShingle BF Psychology
R Medicine (General)
Arulmolly, Annathurai
A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network
description Cardiovascular disease is the leading cause of death, and specialists estimate that roughly half of all heart attacks and strokes happen in individuals who have not been flagged as 'at risk. Hence, there's an urgent need to progress the exactness of heart infection diagnosis. To this end, we explore the potential of utilizing information examination, and in specific the plan and utilize of convolutional neural networks (CNN) for classify heart disease based on ultrasound heart images. Our fundamental commitment is the plan, assessment, and optimization of CNN models of expanding profundity for heart disease classification. Moreover, a system with high precision and accuracy is required to analyse heart disease classification. This study utilized ultrasound scanned heart images which are collected from local hospital. A total of 50 images which includes 20 normal and 30 abnormal heart images. The model has been run successfully without any errors and produce a high accuracy of 96% after running for 46 epochs. Furthermore, a comparative study has been done between SVM and CNN using the similar dataset to analyse performance based on the accuracy.
format Final Year Project Report
author Arulmolly, Annathurai
author_facet Arulmolly, Annathurai
author_sort Arulmolly, Annathurai
title A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network
title_short A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network
title_full A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network
title_fullStr A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network
title_full_unstemmed A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network
title_sort deep learning approach for heart disease classification using convolutional neural network
publisher Universiti Malaysia Sarawak (UNIMAS)
publishDate 2019
url http://ir.unimas.my/id/eprint/27551/1/A%20deep%20learning%20approach%20for%20heart%20disease%20classification%20using...%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/27551/8/Arulmolly%20Annathurai%20ft.pdf
http://ir.unimas.my/id/eprint/27551/
_version_ 1817848716273385472
score 13.222552

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