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Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables

Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-relat...

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Autores principales: Martin-Morales, Agustin, Yamamoto, Masaki, Inoue, Mai, Vu, Thien, Dawadi, Research, Araki, Michihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534618/
https://www.ncbi.nlm.nih.gov/pubmed/37764721
http://dx.doi.org/10.3390/nu15183937
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author Martin-Morales, Agustin
Yamamoto, Masaki
Inoue, Mai
Vu, Thien
Dawadi, Research
Araki, Michihiro
author_facet Martin-Morales, Agustin
Yamamoto, Masaki
Inoue, Mai
Vu, Thien
Dawadi, Research
Araki, Michihiro
author_sort Martin-Morales, Agustin
collection PubMed
description Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-related health data, and a combination of both. Machine learning (ML) models, particularly the random forest algorithm, demonstrated robust consistency across health, nutrition, and mixed categories in predicting death from CVD. Shapley additive explanation (SHAP) values showed age, systolic blood pressure, and several other health factors as crucial variables, while fiber, calcium, and vitamin E, among others, were significant nutritional variables. Our research emphasizes the importance of comprehensive health evaluation and dietary intake in predicting CVD mortality. The inclusion of nutrition variables improved the performance of our models, underscoring the utility of dietary intake in ML-based data analysis. Further investigation using large datasets with recurring dietary recalls is necessary to enhance the effectiveness and interpretability of such models.
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spelling pubmed-105346182023-09-29 Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables Martin-Morales, Agustin Yamamoto, Masaki Inoue, Mai Vu, Thien Dawadi, Research Araki, Michihiro Nutrients Article Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-related health data, and a combination of both. Machine learning (ML) models, particularly the random forest algorithm, demonstrated robust consistency across health, nutrition, and mixed categories in predicting death from CVD. Shapley additive explanation (SHAP) values showed age, systolic blood pressure, and several other health factors as crucial variables, while fiber, calcium, and vitamin E, among others, were significant nutritional variables. Our research emphasizes the importance of comprehensive health evaluation and dietary intake in predicting CVD mortality. The inclusion of nutrition variables improved the performance of our models, underscoring the utility of dietary intake in ML-based data analysis. Further investigation using large datasets with recurring dietary recalls is necessary to enhance the effectiveness and interpretability of such models. MDPI 2023-09-11 /pmc/articles/PMC10534618/ /pubmed/37764721 http://dx.doi.org/10.3390/nu15183937 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
Martin-Morales, Agustin
Yamamoto, Masaki
Inoue, Mai
Vu, Thien
Dawadi, Research
Araki, Michihiro
Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables
title Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables
title_full Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables
title_fullStr Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables
title_full_unstemmed Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables
title_short Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables
title_sort predicting cardiovascular disease mortality: leveraging machine learning for comprehensive assessment of health and nutrition variables
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534618/
https://www.ncbi.nlm.nih.gov/pubmed/37764721
http://dx.doi.org/10.3390/nu15183937
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