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...
Saved in:
| Main Authors: | Hui Jin, Ling Zhang, Yan Sun, Ya Xu, Man Luo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1582779/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The independent and combined effects of blood heavy metal concentrations on all-cause mortality and cardiovascular mortality in adult patients with diabetes mellitus
by: Lipeng Cai, et al.
Published: (2025-06-01) -
Associations between exposure to heavy metal and sarcopenia prevalence: a cross-sectional study using NHANES data
by: Yingying Zhang, et al.
Published: (2025-07-01) -
Using Life’s Essential 8 and heavy metal exposure to determine infertility risk in American women: a machine learning prediction model based on the SHAP method
by: Xiaoqing Gu, et al.
Published: (2025-07-01) -
Toxic hearts: Machine learning uncovers the cardiovascular toll of heavy metals in the Philippine landscape
by: Jose Eric M. Lacsa
Published: (2025-09-01) -
Association between heavy metal exposure and heart failure incidence and mortality: insights from NHANES data (2003–2018)
by: Zebin Lin, et al.
Published: (2025-05-01)