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
相关论文
共 30 条
  • [1] Role of Dietary Salt and Potassium Intake in Cardiovascular Health and Disease: A Review of the Evidence
    Aaron, Kristal J.
    Sanders, Paul W.
    [J]. MAYO CLINIC PROCEEDINGS, 2013, 88 (09) : 987 - 995
  • [2] Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study
    Abdalrada, Ahmad Shaker
    Abawajy, Jemal
    Al-Quraishi, Tahsien
    Islam, Sheikh Mohammed Shariful
    [J]. JOURNAL OF DIABETES AND METABOLIC DISORDERS, 2022, 21 (01) : 251 - 261
  • [3] Diet and Cardiovascular Disease: Effects of Foods and Nutrients in Classical and Emerging Cardiovascular Risk Factors
    Badimon, Lina
    Chagas, Patricia
    Chiva-Blanch, Gemma
    [J]. CURRENT MEDICINAL CHEMISTRY, 2019, 26 (19) : 3639 - 3651
  • [4] Nutrition and Cardiovascular Health
    Casas, Rosa
    Castro-Barquero, Sara
    Estruch, Ramon
    Sacanella, Emilio
    [J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2018, 19 (12)
  • [5] Long chain omega-3 fatty acids and cardiovascular disease: a systematic review
    Delgado-Lista, Javier
    Perez-Martinez, Pablo
    Lopez-Miranda, Jose
    Perez-Jimenez, Francisco
    [J]. BRITISH JOURNAL OF NUTRITION, 2012, 107 : S201 - S213
  • [6] A data-driven approach to predicting diabetes and cardiovascular disease with machine learning
    Dinh, An
    Miertschin, Stacey
    Young, Amber
    Mohanty, Somya D.
    [J]. BMC MEDICAL INFORMATICS AND DECISION MAKING, 2019, 19 (01)
  • [7] Vitamin E: Where Are We Now in Vascular Diseases?
    Garg, Anahita
    Lee, Jetty Chung-Yung
    [J]. LIFE-BASEL, 2022, 12 (02):
  • [8] Kirasich K., 2018, SMU Data Science Review, V1, P9
  • [9] Machine Learning in Nutrition Research
    Kirk, Daniel
    Kok, Esther
    Tufano, Michele
    Tekinerdogan, Bedir
    Feskens, Edith J. M.
    Camps, Guido
    [J]. ADVANCES IN NUTRITION, 2022, 13 (06) : 2573 - 2589
  • [10] Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records
    Liu, Jundong
    Chou, Elizabeth L.
    Lau, Kui Kai
    Woo, Peter Y. M.
    Li, Jun
    Chan, Kei Hang Katie
    [J]. JOURNAL OF THE NEUROLOGICAL SCIENCES, 2022, 440