Heartbeat sound feature extraction and classification
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| Առցանց հասանելիություն: | http://hdl.handle.net/10361/14053 |
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10361-140532022-01-26T10:18:27Z Heartbeat sound feature extraction and classification Bibrity, Fariha Chowdhury Jahan, Farhana Khan, Md. Shahriar Uddin, Jia Department of Computer Science and Engineering, Brac University Heart beat Neural network Classification Features Extraction This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 27-30). Heart diseases has ranked top as the cause of death globally. The harsh truth is, in this time it is hard to get proper medical treatment in proper time and still it is costly. Now the only light of hope is coming from technology. Heart sound is one of the oldest ways to judge the condition of the heart. This paper shows the outcomes from a set of extracted features of Heartbeat sound by applying the classifier Na¨ıve Bayes, Neural Network, Decision Tree, SVM, Logistic Regression and Nearest Neighbor. Experimental results show that SVM carried the highest accuracy (i.e., 76%) for normal and abnormal heartbeat classification, ANN (i.e., 83%) for normal and murmur classification and Nearest Neighbor (i.e., 73%) for normal and extrasystole classification compared to other machine learning algorithms .This research includes comparing the results from all this algorithms and finding the best possible set of data and algorithms. This machine learning technique contributes to the development of heart disease related researches and developing more efficient machines to detect heart diseases accurately in short time. Fariha Chowdhury Bibrity Farhana Jahan Md. Shahriar Khan B. Computer Science 2020-10-11T05:27:47Z 2020-10-11T05:27:47Z 2019 2019-12 Thesis ID: 19101677 ID: 18341012 ID: 14301033 http://hdl.handle.net/10361/14053 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 30 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Heart beat Neural network Classification Features Extraction |
| spellingShingle |
Heart beat Neural network Classification Features Extraction Bibrity, Fariha Chowdhury Jahan, Farhana Khan, Md. Shahriar Heartbeat sound feature extraction and classification |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Uddin, Jia |
| author_facet |
Uddin, Jia Bibrity, Fariha Chowdhury Jahan, Farhana Khan, Md. Shahriar |
| format |
Thesis |
| author |
Bibrity, Fariha Chowdhury Jahan, Farhana Khan, Md. Shahriar |
| author_sort |
Bibrity, Fariha Chowdhury |
| title |
Heartbeat sound feature extraction and classification |
| title_short |
Heartbeat sound feature extraction and classification |
| title_full |
Heartbeat sound feature extraction and classification |
| title_fullStr |
Heartbeat sound feature extraction and classification |
| title_full_unstemmed |
Heartbeat sound feature extraction and classification |
| title_sort |
heartbeat sound feature extraction and classification |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/14053 |
| work_keys_str_mv |
AT bibrityfarihachowdhury heartbeatsoundfeatureextractionandclassification AT jahanfarhana heartbeatsoundfeatureextractionandclassification AT khanmdshahriar heartbeatsoundfeatureextractionandclassification |
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1814308889074597888 |