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

被引:10
|
作者
Martin-Morales, Agustin [1 ,2 ]
Yamamoto, Masaki [1 ,2 ]
Inoue, Mai [1 ,2 ]
Vu, Thien [1 ,2 ]
Dawadi, Research [1 ,2 ]
Araki, Michihiro [1 ,2 ,3 ,4 ]
机构
[1] Natl Inst Biomed Innovat Hlth & Nutr, Artificial Intelligence Ctr Hlth & Biomed Res, 3-17 Senrioka shinmachi, Settsu 5660002, Japan
[2] Natl Cerebral & Cardiovasc Ctr, Biobank, 6-1 Kishibe Shinmachi, Suita, Osaka 5648565, Japan
[3] Kyoto Univ, Grad Sch Med, 54 Kawahara Cho,Sakyo Ku, Kyoto 6068507, Japan
[4] Kobe Univ, Grad Sch Sci Technol & Innovat, 1-1 Rokkodai Cho,Nada Ku, Kobe 6578501, Japan
基金
日本科学技术振兴机构;
关键词
machine learning; cardiovascular disease; prediction model; nutrition; dietary features; SHAP; RISK;
D O I
10.3390/nu15183937
中图分类号
R15 [营养卫生、食品卫生]; TS201 [基础科学];
学科分类号
100403 ;
摘要
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.
引用
收藏
页数:13
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