Environmental chemical exposures and a machine learning-based model for predicting hypertension in NHANES 2003–2016

Abstract

Abstract Background 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. Objective To investigate the role of environmental chemical exposures in predicting hypertension. Methods 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. Results 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 R2 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. Conclusions 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.

Keywords

• Environmental chemicals
• Hypertension
• Machine learning
• Multiple imputation