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Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417090/ https://www.ncbi.nlm.nih.gov/pubmed/37568902 http://dx.doi.org/10.3390/diagnostics13152540 |
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author | Mahmud, Istiak Kabir, Md Mohsin Mridha, M. F. Alfarhood, Sultan Safran, Mejdl Che, Dunren |
author_facet | Mahmud, Istiak Kabir, Md Mohsin Mridha, M. F. Alfarhood, Sultan Safran, Mejdl Che, Dunren |
author_sort | Mahmud, Istiak |
collection | PubMed |
description | Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%. |
format | Online Article Text |
id | pubmed-10417090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104170902023-08-12 Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel Mahmud, Istiak Kabir, Md Mohsin Mridha, M. F. Alfarhood, Sultan Safran, Mejdl Che, Dunren Diagnostics (Basel) Article Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%. MDPI 2023-07-31 /pmc/articles/PMC10417090/ /pubmed/37568902 http://dx.doi.org/10.3390/diagnostics13152540 Text en © 2023 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 Mahmud, Istiak Kabir, Md Mohsin Mridha, M. F. Alfarhood, Sultan Safran, Mejdl Che, Dunren Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel |
title | Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel |
title_full | Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel |
title_fullStr | Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel |
title_full_unstemmed | Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel |
title_short | Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel |
title_sort | cardiac failure forecasting based on clinical data using a lightweight machine learning metamodel |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417090/ https://www.ncbi.nlm.nih.gov/pubmed/37568902 http://dx.doi.org/10.3390/diagnostics13152540 |
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