Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications

Cardiovascular Toxicology · Published 2026-06-01 · DOI 10.1007/s12012-026-10142-7

Authors (11)

Ahmed Farid Gadelmawla, Amal A. Alsubaiei, Najat Y. AlSejari, Mohanad A. Alkuwaiti, Bayan Mahafdah, Hamza A. Abdul-Hafez, Ahmed Elmorsy Mohamed, Ahmed W. Hageen, Abdullah M. Alharran, Giuseppe Andò, Wilbert S. Aronow

Abstract

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Publication details

Year

2026

Citation

Gadelmawla, A., Alsubaiei, A., AlSejari, N., et al. (2026). Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications. Cardiovascular Toxicology. https://doi.org/10.1007/s12012-026-10142-7

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