ArticleFrontiers in public health2025
Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.Cardiovascular toxicology · 2026Review
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Association between the monocyte-to-lymphocyte ratio and 28-day all-cause mortality in sepsis-associated delirium patients: a retrospective study and machine learning.BMC infectious diseases · 2026Article
- Development and Validation of Time-to-Event Machine Learning Models for Predicting Disease-Free Survival in Patients with Locally Advanced Colorectal Cancer: A Multicenter Cohort Study.Annals of surgical oncology · 2026Article
- TyG-ABSI as a novel metabolic obesity indicator for carotid plaque: an explainable machine learning study using SHAP in low-income population.BMC endocrine disorders · 2025Article
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Authors and funding
5 authors.
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Abstract
Background: Environmental exposure to heavy metals, such as arsenic, cadmium, and lead, is a known risk factor for cardiovascular diseases. Objective: We aim to examine the associations between heavy metal exposure and the mortality of patients with cardiovascular diseases. Methods: We analyzed data from the NHANES 2003-2018, including urine and blood metal concentrations from 4,924 participants. Five machine learning models-CoxPHSurvival, FastKernelSurvivalSVM, GradientBoostingSurvival, RandomSurvivalForest, and ExtraSurvivalTrees-were used to predict cardiovascular mortality. Model performance was assessed with the concordance index (C-index), integrated Brier score, time-dependent AUC, and calibration curves. SHAP analysis was conducted using a reduced background dataset created via K-means clustering. Results: GradientBoostingSurvival (GBS) showed the best performance for hypertension (C-index: 0.780, mean AUC: 0.798). RandomSurvivalForest (RSF) was the top model for coronary heart disease (C-index: 0.592, mean AUC: 0.626) and myocardial infarction (C-index: 0.705, mean AUC: 0.743), while CoxPHSurvival excelled for heart failure (C-index: 0.642, mean AUC: 0.672) and stroke (C-index: 0.658, mean AUC: 0.691). ExtraSurvivalTrees performed best in angina (C-index: 0.652, mean AUC: 0.669). Calibration curves confirmed the models' accuracy. SHAP analysis identified age as the most influential factor, with heavy metals like lead, cadmium, and thallium significantly contributing to risk. A user-friendly web calculator was developed for individualized survival predictions. Conclusion: Machine learning models, including GradientBoostingSurvival, RandomSurvivalForest, CoxPHSurvival, and ExtraSurvivalTrees, demonstrated strong performance in predicting mortality risk for various cardiovascular diseases. Key metals were identified as significant risk factors in cardiovascular risk assessment.
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