背景:关于高血压患者中重金属暴露与死亡率之间的联系,现有数据有限。
目的:我们打算建立一种具有高效率和鲁棒性的可解释机器学习(ML)模型,该模型基于高血压患者中的重金属暴露来监测死亡率。
方法:我们的数据集来自美国国家健康和营养调查(NHANES,2013-2018)。我们开发了5ML模型,用于通过重金属暴露预测高血压患者的死亡率,并通过10个辨别特征对它们进行了测试。Further,通过遗传算法(GA)进行参数调整后,选择性能最佳的模型进行预测。最后,为了可视化模型的决策能力,我们使用SHapley加法扩张(SHAP)和局部可解释模型-不可知解释(LIME)算法来说明功能。该研究共包括2347名参与者。
结果:选择了13种重金属对高血压患者死亡率预测的最佳极限梯度增强(XGB)和GA(AUC0.959;95%CI0.953-0.965;准确性96.8%)。根据SHAP值的总和,镉(0.094),钴(2.048),铅(1.12),尿液中的钨(0.129),和铅(2.026),血液中的汞(1.703)对模型有积极影响,而钡(-0.001),钼(-2.066),锑(-0.398),锡(-0.498),尿液中的铊(-2.297),和硒(-0.842),血液中的锰(-1.193)对模型产生负面影响。
结论:高血压患者与重金属暴露相关的死亡率是通过有效的,健壮,具有SHAP和LIME的可解释GA-XGB模型。镉,钴,铅,尿液中的钨,血液中的汞与死亡率呈正相关,而钡,钼,锑,锡,尿液中的铊,和领导,硒,血锰与死亡率呈负相关。
BACKGROUND: There are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients.
OBJECTIVE: We intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that monitors mortality based on heavy metal exposure among hypertension patients.
METHODS: Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2018). We developed 5 ML models for mortality prediction among hypertension patients by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, we chose the optimally performing model after parameter adjustment by genetic algorithm (GA) for prediction. Finally, in order to visualize the model\'s ability to make decisions, we used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 2347 participants in total.
RESULTS: A best-performing eXtreme Gradient Boosting (XGB) with GA for mortality prediction among hypertension patients by 13 heavy metals was selected (AUC 0.959; 95% CI 0.953-0.965; accuracy 96.8%). According to sum of SHAP values, cadmium (0.094), cobalt (2.048), lead (1.12), tungsten (0.129) in urine, and lead (2.026), mercury (1.703) in blood positively influenced the model, while barium (- 0.001), molybdenum (- 2.066), antimony (- 0.398), tin (- 0.498), thallium (- 2.297) in urine, and selenium (- 0.842), manganese (- 1.193) in blood negatively influenced the model.
CONCLUSIONS: Hypertension patients\' mortality associated with heavy metal exposure was predicted by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Cadmium, cobalt, lead, tungsten in urine, and mercury in blood are positively correlated with mortality, while barium, molybdenum, antimony, tin, thallium in urine, and lead, selenium, manganese in blood is negatively correlated with mortality.