Department of Cardiology, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Hospital District, Ouhai District, Wenzhou City, 325000, Zhejiang Province, China.
BMC Cardiovasc Disord. 2024 Oct 9;24(1):544. doi: 10.1186/s12872-024-04216-z.
Hypertension is a common disease, often overlooked in its early stages due to mild symptoms. And persistent elevated blood pressure can lead to adverse outcomes such as coronary heart disease, stroke, and kidney disease. There are many risk factors that lead to hypertension, including various environmental chemicals that humans are exposed to, which are believed to be modifiable risk factors for hypertension.
To investigate the role of environmental chemical exposures in predicting hypertension.
A total of 11,039 eligible participants were obtained from NHANES 2003-2016, and multiple imputation was used to process the missing data, resulting in 5 imputed datasets. 8 Machine learning algorithms were applied to the 5 imputed datasets to establish hypertension prediction models, and the average accuracy score, precision score, recall score, and F1 score were calculated. A generalized linear model was also built to predict the systolic and diastolic blood pressure levels.
All 8 algorithms had good predictions for hypertension, with Support Vector Machine (SVM) being the best, with accuracy, precision, recall, F1 scores and area under the curve (AUC) of 0.751, 0.699, 0.717, 0.708 and 0.822, respectively. The R of the linear model on the training and test sets was 0.28, 0.25 for systolic and 0.06, 0.05 for diastolic blood pressure.
In this study, relatively accurate prediction of hypertension was achieved using environmental chemicals with machine learning algorithms, demonstrating the predictive value of environmental chemicals for hypertension.
高血压是一种常见疾病，由于其早期症状较轻，常被忽视。而持续升高的血压会导致不良后果，如冠心病、中风和肾病。有许多导致高血压的风险因素，包括人类接触的各种环境化学物质，这些因素被认为是高血压的可改变风险因素。
研究环境化学物质暴露对高血压的预测作用。
从 NHANES 2003-2016 中获取了 11039 名符合条件的参与者，使用多重插补处理缺失数据，得到 5 个插补数据集。应用 8 种机器学习算法对 5 个插补数据集建立高血压预测模型，并计算平均准确率、精确率、召回率和 F1 评分。还建立了广义线性模型来预测收缩压和舒张压水平。
所有 8 种算法对高血压的预测都较好，支持向量机（SVM）表现最佳，准确率、精确率、召回率、F1 评分和曲线下面积（AUC）分别为 0.751、0.699、0.717、0.708 和 0.822。线性模型在训练集和测试集上的 R 分别为 0.28、0.25 用于收缩压，0.06、0.05 用于舒张压。
本研究采用机器学习算法，利用环境化学物质对高血压进行了相对准确的预测，表明环境化学物质对高血压具有预测价值。
