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Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction

AIMS: Heart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by co...

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Autores principales: Li, Xuewen, Shang, Chengming, Xu, Changyan, Wang, Yiting, Xu, Jiancheng, Zhou, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463624/
https://www.ncbi.nlm.nih.gov/pubmed/37620904
http://dx.doi.org/10.1186/s12911-023-02240-1
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author Li, Xuewen
Shang, Chengming
Xu, Changyan
Wang, Yiting
Xu, Jiancheng
Zhou, Qi
author_facet Li, Xuewen
Shang, Chengming
Xu, Changyan
Wang, Yiting
Xu, Jiancheng
Zhou, Qi
author_sort Li, Xuewen
collection PubMed
description AIMS: Heart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms. METHODS: Cohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively. RESULTS: The best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits. CONCLUSIONS: This study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.
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spelling pubmed-104636242023-08-30 Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction Li, Xuewen Shang, Chengming Xu, Changyan Wang, Yiting Xu, Jiancheng Zhou, Qi BMC Med Inform Decis Mak Research AIMS: Heart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms. METHODS: Cohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively. RESULTS: The best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits. CONCLUSIONS: This study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms. BioMed Central 2023-08-24 /pmc/articles/PMC10463624/ /pubmed/37620904 http://dx.doi.org/10.1186/s12911-023-02240-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Xuewen
Shang, Chengming
Xu, Changyan
Wang, Yiting
Xu, Jiancheng
Zhou, Qi
Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
title Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
title_full Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
title_fullStr Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
title_full_unstemmed Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
title_short Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
title_sort development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463624/
https://www.ncbi.nlm.nih.gov/pubmed/37620904
http://dx.doi.org/10.1186/s12911-023-02240-1
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