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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Comparison of machine learning techniques to predict cardiovascular disease

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    15101091,17301219,15301083_CSE.pdf (444.6Kb)
    Date
    2018-12
    Publisher
    BRAC University
    Author
    Jaber, Mir Mohammad
    Raad, Tahmid Imam
    Tasneem, Tasfia
    Metadata
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    URI
    http://hdl.handle.net/10361/11408
    Abstract
    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.
    Keywords
    Machine learning; Cardiovascular diseases
     
    LC Subject Headings
    Diseases -- Early detection.; Stroke; etiology; Medical informatics.; Artificial intelligence.; Machine learning.
     
    Description
    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.
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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