全国健康与营养检查调查
可解释性
医学
疾病
环境卫生
心血管健康
机器学习
老年学
计算机科学
内科学
人口
作者
Agustin Martin‐Morales,Masaki Yamamoto,Mai Inoue,Thien Vu,Research Dawadi,Michihiro Araki
出处
期刊:Nutrients
[MDPI AG]
日期:2023-09-11
卷期号:15 (18): 3937-3937
被引量:1
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.
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