Development and validation of a nomogram for arterial stiffness

Wang, Tingjun; Cai, Xiaoqi; Zhang, Lingyu; Yang, Ting; Ye, Chaoyi; Xu, Guoyan; Xie, Liangdi

doi:10.1111/jch.14723

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…The quantity of individuals with hypertension has multiplied since 1990, and 1.28 billion adults worldwide have hypertension currently. 1 Risk factors for hypertension include obesity, lack of physical exercise, alcohol consumption, etc. 2 According to epidemiological data, exposure to environmental metals is associated with hypertension.…”

Section: Introductionmentioning

confidence: 99%

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

Xu,

Sun

2024

J of Clinical Hypertension

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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.

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Section: Introductionmentioning

confidence: 99%

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

Xu,

Sun

2024

J of Clinical Hypertension

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Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension

Xu,

Sun

2024

High Blood Press Cardiovasc Prev

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Introduction There are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients. Aim 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.

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Development and validation of a nomogram for arterial stiffness

Cited by 2 publications

References 26 publications

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension

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