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Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology
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DOI：10.3390/toxics14080683.png)
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Abstract
En 中文
The rapid expansion of machine learning applications in heavy metal risk research has generated a large but fragmented body of literature, necessitating a systematic summary of various methodologies. This survey examines 182 research articles from Web of Science, Scopus, and IEEE Xplore over the past 10 years, providing a comprehensive analysis of the application of prevalent machine learning algorithms across research areas related to heavy metal risks, covering more than 30 algorithms and 9 research areas. The results show that regional risk assessment, risk source analysis, risk driver analysis, and research on the pathogenicity of HMs are the four most frequently applied areas of machine learning, accounting for 80.06% of all applications. Classical machine learning, especially a series of tree-based algorithms, dominates across all applications, accounting for 69.93% and 42.74%, respectively. In addition, some auxiliary algorithms, particularly those for feature analysis, are often used in conjunction with machine learning, primarily to interpret model predictions and analyze risk sources or drivers. Three principal methodological challenges emerge from our review: (1) poor model generalizability across different environmental conditions; (2) insufficient reliability of predictions; and (3) difficulty in obtaining high-quality training data. Some current literature is also constrained by small sample sizes, regional bias, limited field validation, and insufficient integration of multi-omics data with machine learning pipelines. To address these gaps, we advocate for the routine adoption of explainable AI techniques with rigorous stability checks, the development of publicly available, field-validated benchmark datasets, and greater integration of mechanistic knowledge with data-driven models.
Keywords:
machine learning
heavy metal
human health risk
ecological risk
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4.1
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1.2W
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