Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels
BackgroundEnvironmental exposure to heavy metals, such as arsenic, cadmium, and lead, is a known risk factor for cardiovascular diseases.ObjectiveWe aim to examine the associations between heavy metal exposure and the mortality of patients with cardiovascular diseases.MethodsWe analyzed data from th...
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Frontiers Media S.A.
2025-05-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1582779/full |
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| author | Hui Jin Ling Zhang Yan Sun Ya Xu Man Luo |
| author_facet | Hui Jin Ling Zhang Yan Sun Ya Xu Man Luo |
| author_sort | Hui Jin |
| collection | DOAJ |
| description | BackgroundEnvironmental exposure to heavy metals, such as arsenic, cadmium, and lead, is a known risk factor for cardiovascular diseases.ObjectiveWe aim to examine the associations between heavy metal exposure and the mortality of patients with cardiovascular diseases.MethodsWe 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.ResultsGradientBoostingSurvival (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.ConclusionMachine 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. |
| format | Article |
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| issn | 2296-2565 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-f6b725b0affe4e86a8044ffbf788e1662025-08-20T01:55:22ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-05-011310.3389/fpubh.2025.15827791582779Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levelsHui Jin0Ling Zhang1Yan Sun2Ya Xu3Man Luo4Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, ChinaHuai’an No. 3 People’s Hospital, Huaian Second Clinical College, Xuzhou Medical University, Huai’an, Jiangsu, ChinaNanjing Jiangbei Hospital, Affiliated Nanjing Jiangbei Hospital of Xinglin College, Nantong University, Nanjing, Jiangsu, ChinaNanjing Jiangbei Hospital, Affiliated Nanjing Jiangbei Hospital of Xinglin College, Nantong University, Nanjing, Jiangsu, ChinaHuai’an TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Huai'an, Jiangsu, ChinaBackgroundEnvironmental exposure to heavy metals, such as arsenic, cadmium, and lead, is a known risk factor for cardiovascular diseases.ObjectiveWe aim to examine the associations between heavy metal exposure and the mortality of patients with cardiovascular diseases.MethodsWe 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.ResultsGradientBoostingSurvival (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.ConclusionMachine 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1582779/fullinterpretable machine learningheavy metalscardiovascular diseasemortalitySHAP |
| spellingShingle | Hui Jin Ling Zhang Yan Sun Ya Xu Man Luo Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels Frontiers in Public Health interpretable machine learning heavy metals cardiovascular disease mortality SHAP |
| title | Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels |
| title_full | Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels |
| title_fullStr | Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels |
| title_full_unstemmed | Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels |
| title_short | Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels |
| title_sort | developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels |
| topic | interpretable machine learning heavy metals cardiovascular disease mortality SHAP |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1582779/full |
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