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1
Development and validation of risk prediction models for multiple cardiovascular diseases and Type 2 diabetes
Development and validation of risk prediction models for multiple cardiovascular diseases and Type 2 diabetes
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Development and validation of risk prediction models for multiple cardiovascular diseases and Type 2 diabetes

PloS one, 2020-07, Vol.15 (7), p.e0235758-e0235758 [Peer Reviewed Journal]

COPYRIGHT 2020 Public Library of Science ;COPYRIGHT 2020 Public Library of Science ;2020 Rezaee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Rezaee et al 2020 Rezaee et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0235758 ;PMID: 32726343

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2
Comparison of severe maternal morbidity in pregnancy by modified World Health Organization Classification of maternal cardiovascular risk
Comparison of severe maternal morbidity in pregnancy by modified World Health Organization Classification of maternal cardiovascular risk
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Comparison of severe maternal morbidity in pregnancy by modified World Health Organization Classification of maternal cardiovascular risk

The American heart journal, 2022-08, Vol.250, p.11-22 [Peer Reviewed Journal]

2022 ;Copyright © 2022. Published by Elsevier Inc. ;Copyright Elsevier Limited Aug 2022 ;ISSN: 0002-8703 ;EISSN: 1097-6744 ;DOI: 10.1016/j.ahj.2022.04.009 ;PMID: 35526569

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3
Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison
Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison
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Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison

Computers in biology and medicine, 2021-09, Vol.136, p.104672-104672, Article 104672 [Peer Reviewed Journal]

2021 Elsevier Ltd ;2021. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.104672

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4
Prediction of heart disease and classifiers’ sensitivity analysis
Prediction of heart disease and classifiers’ sensitivity analysis
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Prediction of heart disease and classifiers’ sensitivity analysis

BMC bioinformatics, 2020-07, Vol.21 (1), p.1-278, Article 278 [Peer Reviewed Journal]

COPYRIGHT 2020 BioMed Central Ltd. ;2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-020-03626-y ;PMID: 32615980

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5
Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble
Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble
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Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble

BioMed research international, 2020, Vol.2020, p.9816142-10 [Peer Reviewed Journal]

Copyright © 2020 Bayu Adhi Tama et al. ;COPYRIGHT 2020 Hindawi Limited ;Copyright © 2020 Bayu Adhi Tama et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 ;Copyright © 2020 Bayu Adhi Tama et al. 2020 ;ISSN: 2314-6133 ;EISSN: 2314-6141 ;DOI: 10.1155/2020/9816142 ;PMID: 32420387

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6
A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
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A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Frontiers in computational neuroscience, 2021-01, Vol.14, p.564015-564015 [Peer Reviewed Journal]

Copyright © 2021 Wu, Lu, Yang and Wong. ;2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © 2021 Wu, Lu, Yang and Wong. 2021 Wu, Lu, Yang and Wong ;ISSN: 1662-5188 ;EISSN: 1662-5188 ;DOI: 10.3389/fncom.2020.564015 ;PMID: 33469423

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7
Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals
Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals
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Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals

Computers in biology and medicine, 2021-07, Vol.134, p.104457-104457, Article 104457 [Peer Reviewed Journal]

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.104457 ;PMID: 33991857

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8
Disease prediction via Bayesian hyperparameter optimization and ensemble learning
Disease prediction via Bayesian hyperparameter optimization and ensemble learning
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Disease prediction via Bayesian hyperparameter optimization and ensemble learning

BMC research notes, 2020-04, Vol.13 (1), p.205-205, Article 205 [Peer Reviewed Journal]

COPYRIGHT 2020 BioMed Central Ltd. ;COPYRIGHT 2020 BioMed Central Ltd. ;2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 1756-0500 ;EISSN: 1756-0500 ;DOI: 10.1186/s13104-020-05050-0 ;PMID: 32276658

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9
Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region
Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region
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Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region

BMC medical informatics and decision making, 2023-04, Vol.23 (1), p.72-72, Article 72 [Peer Reviewed Journal]

2023. The Author(s). ;COPYRIGHT 2023 BioMed Central Ltd. ;2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2023 ;ISSN: 1472-6947 ;EISSN: 1472-6947 ;DOI: 10.1186/s12911-023-02169-5 ;PMID: 37076833

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10
Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning
Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning
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Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning

Computational intelligence and neuroscience, 2021-07, Vol.2021, p.1-11 [Peer Reviewed Journal]

Copyright © 2021 Rohit Bharti et al. ;Copyright © 2021 Rohit Bharti et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 ;Copyright © 2021 Rohit Bharti et al. 2021 ;ISSN: 1687-5265 ;EISSN: 1687-5273 ;DOI: 10.1155/2021/8387680 ;PMID: 34306056

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11
Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks
Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks
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Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

Bioengineering (Basel), 2018-05, Vol.5 (2), p.35 [Peer Reviewed Journal]

2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2018 by the authors. 2018 ;ISSN: 2306-5354 ;EISSN: 2306-5354 ;DOI: 10.3390/bioengineering5020035 ;PMID: 29734666

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12
Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
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Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review

Computers in biology and medicine, 2020-05, Vol.120, p.103726-103726, Article 103726 [Peer Reviewed Journal]

2020 Elsevier Ltd ;Copyright © 2020 Elsevier Ltd. All rights reserved. ;2020. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2020.103726 ;PMID: 32421643

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13
Construction of a new smooth support vector machine model and its application in heart disease diagnosis
Construction of a new smooth support vector machine model and its application in heart disease diagnosis
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Construction of a new smooth support vector machine model and its application in heart disease diagnosis

PloS one, 2023-02, Vol.18 (2), p.e0280804-e0280804 [Peer Reviewed Journal]

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ;COPYRIGHT 2023 Public Library of Science ;2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 Wang et al 2023 Wang et al ;2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0280804 ;PMID: 36758063

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14
A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network
A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network
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A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network

Sensors (Basel, Switzerland), 2022-12, Vol.22 (24), p.9576 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22249576 ;PMID: 36559944

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15
Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
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Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection

BMC medical informatics and decision making, 2022-03, Vol.22 (1), p.82-82, Article 82 [Peer Reviewed Journal]

2022. The Author(s). ;COPYRIGHT 2022 BioMed Central Ltd. ;2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 1472-6947 ;EISSN: 1472-6947 ;DOI: 10.1186/s12911-022-01821-w ;PMID: 35346181

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16
Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study
Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study
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Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study

PloS one, 2023-08, Vol.18 (8), p.e0290791-e0290791 [Peer Reviewed Journal]

Copyright: © 2023 Dambha-Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ;COPYRIGHT 2023 Public Library of Science ;2023 Dambha-Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 Dambha-Miller et al 2023 Dambha-Miller et al ;2023 Dambha-Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0290791 ;PMID: 37643199

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17
Substance Use Disorders Are Prevalent in Adults With Congenital Heart Disease and Are Associated With Increased Healthcare Use
Substance Use Disorders Are Prevalent in Adults With Congenital Heart Disease and Are Associated With Increased Healthcare Use
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Substance Use Disorders Are Prevalent in Adults With Congenital Heart Disease and Are Associated With Increased Healthcare Use

The American journal of cardiology, 2023-04, Vol.192, p.24-30 [Peer Reviewed Journal]

2023 Elsevier Inc. ;Copyright © 2023 Elsevier Inc. All rights reserved. ;2023. Elsevier Inc. ;ISSN: 0002-9149 ;EISSN: 1879-1913 ;DOI: 10.1016/j.amjcard.2023.01.008 ;PMID: 36709526

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18
Cardiovascular Disease Detection using Ensemble Learning
Cardiovascular Disease Detection using Ensemble Learning
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Cardiovascular Disease Detection using Ensemble Learning

Computational intelligence and neuroscience, 2022-08, Vol.2022, p.1-9 [Peer Reviewed Journal]

Copyright © 2022 Abdullah Alqahtani et al. ;Copyright © 2022 Abdullah Alqahtani et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 ;Copyright © 2022 Abdullah Alqahtani et al. 2022 ;ISSN: 1687-5265 ;EISSN: 1687-5273 ;DOI: 10.1155/2022/5267498 ;PMID: 36017452

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19
Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
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Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation

BMC bioinformatics, 2021-11, Vol.22 (Suppl 5), p.1-93, Article 93 [Peer Reviewed Journal]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-021-04000-2 ;PMID: 34749631

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20
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
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Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

Computers in biology and medicine, 2021-08, Vol.135, p.104418-104418, Article 104418 [Peer Reviewed Journal]

2021 Elsevier Ltd ;2021. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.104418

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