Machine learning approach for ECG analysis and predicting different heart diseases
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Taal: | English |
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BRAC University
2019
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| Online toegang: | http://hdl.handle.net/10361/11409 |
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10361-114092022-01-26T10:13:14Z Machine learning approach for ECG analysis and predicting different heart diseases Tithi, Sushmita Roy Aktar, Afifa Aleem, Fahimul Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University Machine learning ECG Heart diseases Diseases -- Early detection. Medical informatics. Artificial intelligence. Machine learning. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Includes bibliographical references (pages 54-57). Cataloged from PDF version of thesis. In the modern world, there have been some revolutionary advancement in the field of medical science and research and this is no different for electrocardiogram. Electrocardiogram (also abbreviated as ECG) illustrates the electrical activity of one’s heart over a period of time. Over the years, number of people suffering from heart disease have increased to some extent. Therefore, in our research, we aim to design a model using supervised machine learning that can find anomalies in one’s ECG report by analyzing it. We have applied six supervised machine learning algorithms to distinguish between normal and abnormal ECG. In addition, we used them to predict the chances of a patient suffering from a certain disease. We divided our data set into two parts. 75 percent data in one group for training the model and rest 25 percent data in another group for testing. To avoid any kind of anomalies or repetitions, Cross Validation and Random Train-Test Split was used to obtain an answer as accurate as possible. We have compared the results with each other for a better understanding. Sushmita Roy Tithi Afifa Aktar Fahimul Aleem B. Computer Science and Engineering 2019-02-13T07:15:28Z 2019-02-13T07:15:28Z 2018 2018-12 Thesis ID 14201051 ID 15101015 ID 15101126 http://hdl.handle.net/10361/11409 en 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. 57 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine learning ECG Heart diseases Diseases -- Early detection. Medical informatics. Artificial intelligence. Machine learning. |
| spellingShingle |
Machine learning ECG Heart diseases Diseases -- Early detection. Medical informatics. Artificial intelligence. Machine learning. Tithi, Sushmita Roy Aktar, Afifa Aleem, Fahimul Machine learning approach for ECG analysis and predicting different heart diseases |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Tithi, Sushmita Roy Aktar, Afifa Aleem, Fahimul |
| format |
Thesis |
| author |
Tithi, Sushmita Roy Aktar, Afifa Aleem, Fahimul |
| author_sort |
Tithi, Sushmita Roy |
| title |
Machine learning approach for ECG analysis and predicting different heart diseases |
| title_short |
Machine learning approach for ECG analysis and predicting different heart diseases |
| title_full |
Machine learning approach for ECG analysis and predicting different heart diseases |
| title_fullStr |
Machine learning approach for ECG analysis and predicting different heart diseases |
| title_full_unstemmed |
Machine learning approach for ECG analysis and predicting different heart diseases |
| title_sort |
machine learning approach for ecg analysis and predicting different heart diseases |
| publisher |
BRAC University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/11409 |
| work_keys_str_mv |
AT tithisushmitaroy machinelearningapproachforecganalysisandpredictingdifferentheartdiseases AT aktarafifa machinelearningapproachforecganalysisandpredictingdifferentheartdiseases AT aleemfahimul machinelearningapproachforecganalysisandpredictingdifferentheartdiseases |
| _version_ |
1814308057660784640 |