Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction

Jia Zhao,1,2 Pengyu Zhao,3 Chunjie Li,2 Yonghong Hou3 1Graduate School, Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 2Department of Cardiology, Tianjin Chest Hospital, Tianjin, 300222, People’s Republic of China; 3School of Electrical and Information Engineering, Tianjin...

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Autores principales:	Zhao J, Zhao P, Li C, Hou Y
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	stemi in-hospital mortality prediction model optimized machine learning algorithm random under-sampling Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/5994cd077059487e8d0e5b7e4aaceb5f
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spelling	oai:doaj.org-article:5994cd077059487e8d0e5b7e4aaceb5f2021-12-02T15:29:15ZOptimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction1178-203Xhttps://doaj.org/article/5994cd077059487e8d0e5b7e4aaceb5f2021-09-01T00:00:00Zhttps://www.dovepress.com/optimized-machine-learning-models-to-predict-in-hospital-mortality-for-peer-reviewed-fulltext-article-TCRMhttps://doaj.org/toc/1178-203XJia Zhao,1,2 Pengyu Zhao,3 Chunjie Li,2 Yonghong Hou3 1Graduate School, Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 2Department of Cardiology, Tianjin Chest Hospital, Tianjin, 300222, People’s Republic of China; 3School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People’s Republic of ChinaCorrespondence: Chunjie Li; Yonghong Hou Tel +86(022)88185135Fax +86(022)88185338Email lichunjie0227@126.com; houroy@tju.edu.cnPurpose: This study aimed to optimize machine learning (ML) models for predicting in-hospital mortality in patients with ST-segment elevation acute myocardial infarction (STEMI).Patients and Methods: A total of 5708 STEMI patients were enrolled and divided into two groups according to patients’ hospital outcomes. Both groups were randomly split into a training set (75%) and a testing set (25%). Four ML models were trained with data, which applied random under-sampling (RUS). The performance of optimized ML models was evaluated with respect to accuracy, sensitivity, specificity, G-mean and AUC. Two sets of features in chronological order were considered: a full set that included all variables during hospitalization and a simplified set that only included variables prior to reperfusion therapy, and the performance of the prediction models trained with these two sets of features was compared.Results: For the comprehensive metric – G-mean, the models trained with RUS outperformed those without, 80.54% vs 23.31% on average in the full set and 75.72% vs 35.76% on average in the simplified set. For models trained with the full set, the SVM achieved the best performance with 85.62% accuracy, 84.21% sensitivity, 85.66% specificity, 84.93% G-mean and 0.919 AUC. For models trained with the simplified set, the SVM achieved 83.48% G-mean, which was comparable to the models trained using the full set. For the most critical metric – sensitivity, the SVM trained using the simplified set achieved 89.47%, which even exceed the SVM (84.21%), DT (81.58%) and RF (81.58%) trained using the full set.Conclusion: Applying RUS can improve the performance of prediction models, and the models trained with simplified set, which only included variables prior to reperfusion therapy can accurately predict high-risk patients.Keywords: STEMI, in-hospital mortality, prediction model, optimized machine learning algorithm, random under-samplingZhao JZhao PLi CHou YDove Medical Pressarticlestemiin-hospital mortalityprediction modeloptimized machine learning algorithmrandom under-samplingTherapeutics. PharmacologyRM1-950ENTherapeutics and Clinical Risk Management, Vol Volume 17, Pp 951-961 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	stemi in-hospital mortality prediction model optimized machine learning algorithm random under-sampling Therapeutics. Pharmacology RM1-950
spellingShingle	stemi in-hospital mortality prediction model optimized machine learning algorithm random under-sampling Therapeutics. Pharmacology RM1-950 Zhao J Zhao P Li C Hou Y Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction
description	Jia Zhao,1,2 Pengyu Zhao,3 Chunjie Li,2 Yonghong Hou3 1Graduate School, Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 2Department of Cardiology, Tianjin Chest Hospital, Tianjin, 300222, People’s Republic of China; 3School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People’s Republic of ChinaCorrespondence: Chunjie Li; Yonghong Hou Tel +86(022)88185135Fax +86(022)88185338Email lichunjie0227@126.com; houroy@tju.edu.cnPurpose: This study aimed to optimize machine learning (ML) models for predicting in-hospital mortality in patients with ST-segment elevation acute myocardial infarction (STEMI).Patients and Methods: A total of 5708 STEMI patients were enrolled and divided into two groups according to patients’ hospital outcomes. Both groups were randomly split into a training set (75%) and a testing set (25%). Four ML models were trained with data, which applied random under-sampling (RUS). The performance of optimized ML models was evaluated with respect to accuracy, sensitivity, specificity, G-mean and AUC. Two sets of features in chronological order were considered: a full set that included all variables during hospitalization and a simplified set that only included variables prior to reperfusion therapy, and the performance of the prediction models trained with these two sets of features was compared.Results: For the comprehensive metric – G-mean, the models trained with RUS outperformed those without, 80.54% vs 23.31% on average in the full set and 75.72% vs 35.76% on average in the simplified set. For models trained with the full set, the SVM achieved the best performance with 85.62% accuracy, 84.21% sensitivity, 85.66% specificity, 84.93% G-mean and 0.919 AUC. For models trained with the simplified set, the SVM achieved 83.48% G-mean, which was comparable to the models trained using the full set. For the most critical metric – sensitivity, the SVM trained using the simplified set achieved 89.47%, which even exceed the SVM (84.21%), DT (81.58%) and RF (81.58%) trained using the full set.Conclusion: Applying RUS can improve the performance of prediction models, and the models trained with simplified set, which only included variables prior to reperfusion therapy can accurately predict high-risk patients.Keywords: STEMI, in-hospital mortality, prediction model, optimized machine learning algorithm, random under-sampling
format	article
author	Zhao J Zhao P Li C Hou Y
author_facet	Zhao J Zhao P Li C Hou Y
author_sort	Zhao J
title	Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction
title_short	Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction
title_full	Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction
title_fullStr	Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction
title_full_unstemmed	Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction
title_sort	optimized machine learning models to predict in-hospital mortality for patients with st-segment elevation myocardial infarction
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/5994cd077059487e8d0e5b7e4aaceb5f
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Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction

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