Comparison of machine learning techniques to predict cardiovascular disease
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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| Materyal Türü: | Tez |
| Dil: | English |
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BRAC University
2019
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| Online Erişim: | http://hdl.handle.net/10361/11408 |
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10361-11408 |
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10361-114082022-01-26T10:10:26Z Comparison of machine learning techniques to predict cardiovascular disease Jaber, Mir Mohammad Raad, Tahmid Imam Tasneem, Tasfia Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University Machine learning Cardiovascular diseases Diseases -- Early detection. Stroke; etiology 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 (page 36). Cataloged from PDF version of thesis. The purpose of this thesis is to examine and compare the accuracy of different data mining classication systems through different machine learning techniques to predict cardiovascular disease. This comparison shows the different accuracy rates of different techniques and reasons behind their variations. The Cleveland dataset for heart diseases has been used in this study which contains 303 instances. The data has been divided into two sections named as training and testing datasets. The 10- fold Cross Validation has been used here in order to work with the expanded dataset. The k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive Bayes, Logistic Regression and Deep Belief Network machine learning techniques have been investigated in this research. Besides, ensemble learning method voting classifier has been applied on the data set. By the end of the implementation part, we have found Gaussian Naive Bayes is giving the maximum accuracy in our dataset and deep belief network is performing very poor. The reasons of variations of these different techniques by analyzing their characteristics and behavior with respect to the dataset has been understood by the study conducted for this thesis. Mir Mohammad Jaber Tahmid Imam Raad Tasfia Tasneem B. Computer Science and Engineering 2019-02-13T06:56:55Z 2019-02-13T06:56:55Z 2018 2018-12 Thesis ID 15101091 ID 17301219 ID 15301083 http://hdl.handle.net/10361/11408 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. 36 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine learning Cardiovascular diseases Diseases -- Early detection. Stroke; etiology Medical informatics. Artificial intelligence. Machine learning. |
| spellingShingle |
Machine learning Cardiovascular diseases Diseases -- Early detection. Stroke; etiology Medical informatics. Artificial intelligence. Machine learning. Jaber, Mir Mohammad Raad, Tahmid Imam Tasneem, Tasfia Comparison of machine learning techniques to predict cardiovascular disease |
| 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 Jaber, Mir Mohammad Raad, Tahmid Imam Tasneem, Tasfia |
| format |
Thesis |
| author |
Jaber, Mir Mohammad Raad, Tahmid Imam Tasneem, Tasfia |
| author_sort |
Jaber, Mir Mohammad |
| title |
Comparison of machine learning techniques to predict cardiovascular disease |
| title_short |
Comparison of machine learning techniques to predict cardiovascular disease |
| title_full |
Comparison of machine learning techniques to predict cardiovascular disease |
| title_fullStr |
Comparison of machine learning techniques to predict cardiovascular disease |
| title_full_unstemmed |
Comparison of machine learning techniques to predict cardiovascular disease |
| title_sort |
comparison of machine learning techniques to predict cardiovascular disease |
| publisher |
BRAC University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/11408 |
| work_keys_str_mv |
AT jabermirmohammad comparisonofmachinelearningtechniquestopredictcardiovasculardisease AT raadtahmidimam comparisonofmachinelearningtechniquestopredictcardiovasculardisease AT tasneemtasfia comparisonofmachinelearningtechniquestopredictcardiovasculardisease |
| _version_ |
1814307714088566784 |