Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction

Abstract Background Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial...

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Autores principales:	Suhuai Wang, Jingjie Li, Lin Sun, Jianing Cai, Shihui Wang, Linwen Zeng, Shaoqing Sun
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Machine learning Arrhythmia Acute myocardial infarction Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/1408ba90f524444fb200c1a0479eda8a
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spelling	oai:doaj.org-article:1408ba90f524444fb200c1a0479eda8a2021-11-08T10:59:27ZApplication of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction10.1186/s12911-021-01667-81472-6947https://doaj.org/article/1408ba90f524444fb200c1a0479eda8a2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01667-8https://doaj.org/toc/1472-6947Abstract Background Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI). Methods A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git) . The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set. Results Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI. Conclusions We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research. Trial registration Clinical Trial Registry No.: ChiCTR2100041960.Suhuai WangJingjie LiLin SunJianing CaiShihui WangLinwen ZengShaoqing SunBMCarticleMachine learningArrhythmiaAcute myocardial infarctionComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Arrhythmia Acute myocardial infarction Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Machine learning Arrhythmia Acute myocardial infarction Computer applications to medicine. Medical informatics R858-859.7 Suhuai Wang Jingjie Li Lin Sun Jianing Cai Shihui Wang Linwen Zeng Shaoqing Sun Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
description	Abstract Background Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI). Methods A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git) . The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set. Results Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI. Conclusions We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research. Trial registration Clinical Trial Registry No.: ChiCTR2100041960.
format	article
author	Suhuai Wang Jingjie Li Lin Sun Jianing Cai Shihui Wang Linwen Zeng Shaoqing Sun
author_facet	Suhuai Wang Jingjie Li Lin Sun Jianing Cai Shihui Wang Linwen Zeng Shaoqing Sun
author_sort	Suhuai Wang
title	Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
title_short	Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
title_full	Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
title_fullStr	Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
title_full_unstemmed	Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
title_sort	application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
publisher	BMC
publishDate	2021
url	https://doaj.org/article/1408ba90f524444fb200c1a0479eda8a
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Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction

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