Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
Chemosphere. 2023 Oct;337:139435. doi: 10.1016/j.chemosphere.2023.139435. Epub 2023 Jul 6.
Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 μg/dL) and urinary Cd (0.06-0.15 μg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 μg/L) and urinary Co (0.02-0.32 μg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.
重金属暴露是高血压的常见危险因素。为了基于重金属暴露水平开发一个可解释的预测性机器学习 (ML) 模型来预测高血压，本研究使用了 NHANES（2003-2016）的数据。随机森林 (RF)、支持向量机 (SVM)、决策树 (DT)、多层感知机 (MLP)、岭回归 (RR)、AdaBoost (AB)、梯度提升决策树 (GBDT)、投票分类器 (VC) 和 K 近邻 (KNN) 算法被用于生成高血压的最优预测模型。三种可解释方法，即排列特征重要性分析、偏依赖图 (PDP) 和 Shapley 加性解释 (SHAP) 方法，被整合到一个管道中，并嵌入 ML 中进行模型解释。共有 9005 名符合条件的个体被随机分配到两个不同的集合中进行预测模型的训练和验证。结果表明，在预测模型中，RF 模型表现最佳，在验证集中的准确率为 77.40%。该模型的 AUC 和 F1 评分分别为 0.84 和 0.76。血液 Pb、尿 Cd、尿 Tl 和尿 Co 水平被确定为高血压的主要影响因素，其贡献权重分别为 0.0504±0.0482、0.0389±0.0256、0.0307±0.0179 和 0.0296±0.0162。血液 Pb（0.55-2.93μg/dL）和尿 Cd（0.06-0.15μg/L）水平在特定值范围内显示出与高血压风险最显著的上升趋势，而尿 Tl（0.06-0.26μg/L）和尿 Co（0.02-0.32μg/L）水平则呈下降趋势。协同效应的研究结果表明，Pb 和 Cd 是高血压的主要决定因素。我们的研究结果强调了重金属对高血压的预测价值。通过使用可解释方法，我们发现 Pb、Cd、Tl 和 Co 在预测模型中是重要的贡献因素。
