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

Arulmolly, Annathurai (2019) A Deep Learning Approach For Heart Disease Classification Using Convolutional Neural Network. [Final Year Project Report] (Unpublished)

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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.

Item Type: Final Year Project Report
Additional Information: Project Report (BSc.) - Universiti Malaysia Sarawak, 2019.
Uncontrolled Keywords: Cardiovascular disease, convolutional neural networks (CNN) , ultrasound heart, infection diagnosis, heart attacks and strokes, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, undergraduate, research, Universiti Malaysia Sarawak.
Subjects: B Philosophy. Psychology. Religion > BF Psychology
R Medicine > R Medicine (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Gani
Date Deposited: 22 Oct 2019 02:32
Last Modified: 22 Oct 2019 02:32
URI: http://ir.unimas.my/id/eprint/27551

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