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

Abstract Background Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to det...

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Autores principales:	Cai Wu, Maxwell Hwang, Tian-Hsiang Huang, Yen-Ming J. Chen, Yiu-Jen Chang, Tsung-Han Ho, Jian Huang, Kao-Shing Hwang, Wen-Hsien Ho
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Atrial fibrillation Electrocardiogram Artificial intelligence Ensemble learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/dc335e28fd3c4e2a84533e1c01858bbd
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spelling	oai:doaj.org-article:dc335e28fd3c4e2a84533e1c01858bbd2021-11-14T12:13:05ZApplication of artificial intelligence ensemble learning model in early prediction of atrial fibrillation10.1186/s12859-021-04000-21471-2105https://doaj.org/article/dc335e28fd3c4e2a84533e1c01858bbd2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04000-2https://doaj.org/toc/1471-2105Abstract Background Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. Results This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. Conclusion In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model’s predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.Cai WuMaxwell HwangTian-Hsiang HuangYen-Ming J. ChenYiu-Jen ChangTsung-Han HoJian HuangKao-Shing HwangWen-Hsien HoBMCarticleAtrial fibrillationElectrocardiogramArtificial intelligenceEnsemble learningComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss S5, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Atrial fibrillation Electrocardiogram Artificial intelligence Ensemble learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Atrial fibrillation Electrocardiogram Artificial intelligence Ensemble learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Cai Wu Maxwell Hwang Tian-Hsiang Huang Yen-Ming J. Chen Yiu-Jen Chang Tsung-Han Ho Jian Huang Kao-Shing Hwang Wen-Hsien Ho Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
description	Abstract Background Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. Results This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. Conclusion In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model’s predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.
format	article
author	Cai Wu Maxwell Hwang Tian-Hsiang Huang Yen-Ming J. Chen Yiu-Jen Chang Tsung-Han Ho Jian Huang Kao-Shing Hwang Wen-Hsien Ho
author_facet	Cai Wu Maxwell Hwang Tian-Hsiang Huang Yen-Ming J. Chen Yiu-Jen Chang Tsung-Han Ho Jian Huang Kao-Shing Hwang Wen-Hsien Ho
author_sort	Cai Wu
title	Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
title_short	Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
title_full	Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
title_fullStr	Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
title_full_unstemmed	Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
title_sort	application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation
publisher	BMC
publishDate	2021
url	https://doaj.org/article/dc335e28fd3c4e2a84533e1c01858bbd
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Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation

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