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    Heart Disease Identification Based on Butterfly Optimization and Machine Learning

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    Author
    Asrar, Manal
    Bawazir, Joud
    I Khan, Sibghatullah
    Mian Qaisar, Saeed
    Subject
    Machine learning
    Classification
    Algorithms
    Butterfly Optimization
    Variational Mode Decomposition
    Heart diseases
    PCG
    Date
    2023-04-05
    
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    Abstract
    This paper aims to make use of The Physionet Challenge 2016 collection of normal and abnormal heart sound recordings that were classified by automated identification of PCG sounds to help detect heart diseases earlier and prevent incidents. People with heart diseases have been increasing and most of them lead to fatalities so the detection of sound signals through PCGs can be applied in machine learning models by extracting features from data. In this study, the dataset's recordings are segmented to be used in variational mode decomposition. Once they are decomposed, those means will be fused together into a set of features given to the Butterfly Optimization Algorithm which will conduct a selection of features. As the features are selected, MATLAB was used to test various machine learning algorithms. Results from Support Vector Machines (SVM) and artificial neural networks were used in this investigation (ANN). The ANN model, which had an accuracy rate of 94.8%, was the most accurate of them.
    Department
    Electrical and Computer Engineering
    Publisher
    IEEE
    Sponsor
    Effat University
    DOI
    https://doi.org/10.1109/ICSCA57840.2023.10087885
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1109/ICSCA57840.2023.10087885
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