Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1785112436983988224 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10534618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT martinmoralesagustin predictingcardiovasculardiseasemortalityleveragingmachinelearningforcomprehensiveassessmentofhealthandnutritionvariables AT yamamotomasaki predictingcardiovasculardiseasemortalityleveragingmachinelearningforcomprehensiveassessmentofhealthandnutritionvariables AT inouemai predictingcardiovasculardiseasemortalityleveragingmachinelearningforcomprehensiveassessmentofhealthandnutritionvariables AT vuthien predictingcardiovasculardiseasemortalityleveragingmachinelearningforcomprehensiveassessmentofhealthandnutritionvariables AT dawadiresearch predictingcardiovasculardiseasemortalityleveragingmachinelearningforcomprehensiveassessmentofhealthandnutritionvariables AT arakimichihiro predictingcardiovasculardiseasemortalityleveragingmachinelearningforcomprehensiveassessmentofhealthandnutritionvariables |