Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

Abstract Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs...

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Autores principales:	Woojoo Lee, Joongyub Lee, Seoung-Il Woo, Seong Huan Choi, Jang-Whan Bae, Seungpil Jung, Myung Ho Jeong, Won Kyung Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c96eb90177b74ed6aa1a14aa32c5d091
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spelling	oai:doaj.org-article:c96eb90177b74ed6aa1a14aa32c5d0912021-12-02T17:41:07ZMachine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction10.1038/s41598-021-92362-12045-2322https://doaj.org/article/c96eb90177b74ed6aa1a14aa32c5d0912021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92362-1https://doaj.org/toc/2045-2322Abstract Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors.Woojoo LeeJoongyub LeeSeoung-Il WooSeong Huan ChoiJang-Whan BaeSeungpil JungMyung Ho JeongWon Kyung LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Woojoo Lee Joongyub Lee Seoung-Il Woo Seong Huan Choi Jang-Whan Bae Seungpil Jung Myung Ho Jeong Won Kyung Lee Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction
description	Abstract Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors.
format	article
author	Woojoo Lee Joongyub Lee Seoung-Il Woo Seong Huan Choi Jang-Whan Bae Seungpil Jung Myung Ho Jeong Won Kyung Lee
author_facet	Woojoo Lee Joongyub Lee Seoung-Il Woo Seong Huan Choi Jang-Whan Bae Seungpil Jung Myung Ho Jeong Won Kyung Lee
author_sort	Woojoo Lee
title	Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction
title_short	Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction
title_full	Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction
title_fullStr	Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction
title_full_unstemmed	Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction
title_sort	machine learning enhances the performance of short and long-term mortality prediction model in non-st-segment elevation myocardial infarction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c96eb90177b74ed6aa1a14aa32c5d091
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Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

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