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Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicti...

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Autores principales: Huang, Yen-Chun, Li, Shao-Jung, Chen, Mingchih, Lee, Tian-Shyug, Chien, Yu-Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151160/
https://www.ncbi.nlm.nih.gov/pubmed/34067148
http://dx.doi.org/10.3390/healthcare9050547
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author Huang, Yen-Chun
Li, Shao-Jung
Chen, Mingchih
Lee, Tian-Shyug
Chien, Yu-Ning
author_facet Huang, Yen-Chun
Li, Shao-Jung
Chen, Mingchih
Lee, Tian-Shyug
Chien, Yu-Ning
author_sort Huang, Yen-Chun
collection PubMed
description Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.
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spelling pubmed-81511602021-05-27 Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients Huang, Yen-Chun Li, Shao-Jung Chen, Mingchih Lee, Tian-Shyug Chien, Yu-Ning Healthcare (Basel) Article Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible. MDPI 2021-05-07 /pmc/articles/PMC8151160/ /pubmed/34067148 http://dx.doi.org/10.3390/healthcare9050547 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Yen-Chun
Li, Shao-Jung
Chen, Mingchih
Lee, Tian-Shyug
Chien, Yu-Ning
Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
title Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
title_full Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
title_fullStr Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
title_full_unstemmed Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
title_short Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
title_sort machine-learning techniques for feature selection and prediction of mortality in elderly cabg patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151160/
https://www.ncbi.nlm.nih.gov/pubmed/34067148
http://dx.doi.org/10.3390/healthcare9050547
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