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Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique

The machine learning (ML)-based classification models are widely utilized for the automated detection of heart diseases (HDs) using various physiological signals such as electrocardiogram (ECG), magnetocardiography (MCG), heart sound (HS), and impedance cardiography (ICG) signals. However, ECG-based...

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Autores principales: Rath, Adyasha, Mishra, Debahuti, Panda, Ganapati
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9589052/
https://www.ncbi.nlm.nih.gov/pubmed/36299660
http://dx.doi.org/10.3389/fdata.2022.1021518
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author Rath, Adyasha
Mishra, Debahuti
Panda, Ganapati
author_facet Rath, Adyasha
Mishra, Debahuti
Panda, Ganapati
author_sort Rath, Adyasha
collection PubMed
description The machine learning (ML)-based classification models are widely utilized for the automated detection of heart diseases (HDs) using various physiological signals such as electrocardiogram (ECG), magnetocardiography (MCG), heart sound (HS), and impedance cardiography (ICG) signals. However, ECG-based HD identification is the most common one used by clinicians. In the current investigation, the ECG records or subjects have been sampled and are used as inputs to the classification model to distinguish between normal and abnormal patients. The study has employed an imbalanced number of ECG samples for training the various classification models. Few ML methods such as support vector machine (SVM), logistic regression (LR), and adaptive boosting (AdaBoost) which have been rarely used for HD detection have been selected. The performance of the developed model has been evaluated in terms of accuracy, F1-score, and area under curve (AUC) values using ECG signals of subjects given in publicly available (PTB-ECG, MIT-BIH) datasets. Ranking of the models has been assigned based on these performance metrics and it is found that the AdaBoost and LR classifiers stand in first and second positions. These two models have been ensembled based on the majority voting principle and the performance measure of this ensemble model has also been determined. It is, in general, observed that the proposed ensemble model demonstrates the best HD detection performance of 0.946, 0.949, and 0.951 for the PTB-ECG dataset and 0.921, 0.926, and 0.950 for the MIT-BIH dataset in terms of accuracy, F1-score, and AUC, respectively. The proposed methodology can also be employed for the classification of HD using ICG, MCG, and HS signals as inputs. Further, the proposed methodology can also be applied to the detection of other diseases.
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spelling pubmed-95890522022-10-25 Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique Rath, Adyasha Mishra, Debahuti Panda, Ganapati Front Big Data Big Data The machine learning (ML)-based classification models are widely utilized for the automated detection of heart diseases (HDs) using various physiological signals such as electrocardiogram (ECG), magnetocardiography (MCG), heart sound (HS), and impedance cardiography (ICG) signals. However, ECG-based HD identification is the most common one used by clinicians. In the current investigation, the ECG records or subjects have been sampled and are used as inputs to the classification model to distinguish between normal and abnormal patients. The study has employed an imbalanced number of ECG samples for training the various classification models. Few ML methods such as support vector machine (SVM), logistic regression (LR), and adaptive boosting (AdaBoost) which have been rarely used for HD detection have been selected. The performance of the developed model has been evaluated in terms of accuracy, F1-score, and area under curve (AUC) values using ECG signals of subjects given in publicly available (PTB-ECG, MIT-BIH) datasets. Ranking of the models has been assigned based on these performance metrics and it is found that the AdaBoost and LR classifiers stand in first and second positions. These two models have been ensembled based on the majority voting principle and the performance measure of this ensemble model has also been determined. It is, in general, observed that the proposed ensemble model demonstrates the best HD detection performance of 0.946, 0.949, and 0.951 for the PTB-ECG dataset and 0.921, 0.926, and 0.950 for the MIT-BIH dataset in terms of accuracy, F1-score, and AUC, respectively. The proposed methodology can also be employed for the classification of HD using ICG, MCG, and HS signals as inputs. Further, the proposed methodology can also be applied to the detection of other diseases. Frontiers Media S.A. 2022-10-10 /pmc/articles/PMC9589052/ /pubmed/36299660 http://dx.doi.org/10.3389/fdata.2022.1021518 Text en Copyright © 2022 Rath, Mishra and Panda. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Rath, Adyasha
Mishra, Debahuti
Panda, Ganapati
Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
title Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
title_full Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
title_fullStr Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
title_full_unstemmed Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
title_short Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
title_sort imbalanced ecg signal-based heart disease classification using ensemble machine learning technique
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9589052/
https://www.ncbi.nlm.nih.gov/pubmed/36299660
http://dx.doi.org/10.3389/fdata.2022.1021518
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