ArticleNutrients2023
Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables.
Article in Nutrients, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- In-hospital mortality risk prediction models for patients with acute coronary syndrome: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.Journal of food science · 2026Review
- Artificial Intelligence-Assisted Design of Plant-Protein Meat Analogues: Integrating Nutrition, Functionality, and Fibrillation-Based Texturization.Comprehensive reviews in food science and food safety · 2026Review
- Muscle loss must be interpreted in the context of fluid imbalance: a dual-parameter model predicts functional outcomes after acute stroke.European geriatric medicine · 2026Article
- Toward practical screening of mortality risk: Insights from interpretable machine learning in NHANES.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Predicting the course of high-impact chronic pain using machine learning algorithms.The journal of pain · 2026Article
- Development of a web platform for predicting fall risk in cardiovascular patients using machine learning.Scientific reports · 2026Article
- Web-based cardiovascular disease risk prediction using machine learning.Frontiers in artificial intelligence · 2026Article
- Do nutritional variables improve cardiovascular disease prediction? A comparative machine learning analysis.Frontiers in nutrition · 2026Article
- Predicting anti-CCP positivity and early rheumatoid arthritis onset from routine laboratory parameters: a SHAP-explained machine learning pipeline.Frontiers in medicine · 2026Article
- Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm.Scientific reports · 2025Article
- Association of dietary quality, biological aging, progression and mortality of cardiovascular-kidney-metabolic syndrome: insights from mediation and machine learning approaches.Nutrition journal · 2025Article
- Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.JMIR cardio · 2025Article
- Development of a Machine Learning Tool for Home-Based Assessment of Periodontitis.medRxiv : the preprint server for health sciences · 2025Article
- An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.BMC medical informatics and decision making · 2025Article
- Nutritional intelligence in the food system: Combining food, health, data and AI expertise.Nutrition bulletin · 2025Review
- Prediction of depressive disorder using machine learning approaches: findings from the NHANES.BMC medical informatics and decision making · 2025Article
- A review of six bioactive compounds from preclinical studies as potential breast cancer inhibitors.Molecular biology reports · 2025Review
- The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES.BMC public health · 2025Article
- Association of HbA1c/HDL-C ratio and depression with cardiometabolic multimorbidity in middle-aged and older adults: a nationwide prospective cohort study.Frontiers in nutrition · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.