There are limited data available regarding the connection between hypertension and heavy metal exposure. The authors intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that identifies hypertension based on heavy metal exposure. Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013–2020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19 368 participants in total. A best-performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772–0.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (−0.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension.

中文翻译：

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与金属混合物相关的可解释机器学习模型，通过 EMR 挖掘方法识别高血压

关于高血压和重金属暴露之间的联系的可用数据有限。作者打算建立一种高效且稳健的可解释机器学习（ML）模型，根据重金属暴露来识别高血压。我们的数据集来自美国国家健康和营养检查调查（NHANES，2013-2020.3）。作者开发了 5 个通过重金属暴露来识别高血压的 ML 模型，并通过 10 个判别特征对其进行了测试。进一步，作者通过遗传算法（GA）选择参数调整后性能最优的模型进行识别。最后，为了可视化模型的决策能力，作者使用 SHapley Additive exPlanation (SHAP) 和 Local Interpretable Model-Agnostic Explanations (LIME) 算法来说明特征。该研究总共包括 19 368 名参与者。选择了性能最佳的 eXtreme Gradient Boosting (XGB) 和 GA 来识别 16 种重金属的高血压（AUC：0.774；95% CI：0.772–0.776；准确度：87.7%）。根据SHAP值，尿液中的钡(0.02)、镉(0.017)、铅(0.017)、锑(0.008)、锡(0.007)、锰(0.006)、铊(0.004)、钨(0.004)和铅(血液中的汞（0.048）、汞（0.035）、硒（0.05）、锰（0.007）对模型产生积极影响，而尿液中的镉（-0.001）对模型产生消极影响。研究参与者的高血压与重金属暴露相关，通过使用 SHAP 和 LIME 的高效、稳健且可解释的 GA-XGB 模型来确定。尿液中的钡、镉、铅、锑、锡、锰、铊、钨以及血液中的铅、汞、硒、锰与高血压呈正相关，而血液中的镉则与高血压呈负相关。