Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

Abstract

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry. Binary logistic regression analysis was performed to assess the statistical significance of prehypertension and hypertension, and prediction models were developed using logistic regression, naïve Bayes, and decision trees. Among all risk factors for prehypertension, body mass index (BMI) was identified as the best indicator in both men [odds ratio (OR) = 1.429, 95% confidence interval (CI) = 1.304⁻1.462)] and women (OR = 1.428, 95% CI = 1.204⁻1.453). In contrast, among all risk factors for hypertension, BMI (OR = 1.993, 95% CI = 1.818⁻2.186) was found to be the best indicator in men, whereas the waist-to-height ratio (OR = 2.071, 95% CI = 1.884⁻2.276) was the best indicator in women. In the prehypertension prediction model, men exhibited an area under the receiver operating characteristic curve (AUC) of 0.635, and women exhibited a predictive power with an AUC of 0.777. In the hypertension prediction model, men exhibited an AUC of 0.700, and women exhibited an AUC of 0.845. This study proposes various risk factors for prehypertension and hypertension, and our findings can be used as a large-scale screening tool for controlling and managing hypertension.

Keywords: anthropometry; feature selection; hypertension; machine learning; prehypertension; spirometry.

Figure 1

Sample selection procedure.

Figure 2

Predictive power of each prehypertension prediction model for men and women. Each graph presents the results for the LR, NB, and DT algorithms. (a,b) present the results for men, and (c,d) present the results for women. (a,c) show the results obtained with CFS, and (b,d) show the results obtained with WFS. Abbreviations: LR, logistic regression; NB, naïve Bayes; DT, decision tree; AUC, area under the receiver operating characteristic curve; PHTN, prehypertension; CFS, correlation-based Feature selection; WFS, wrapper-based feature selection.

Figure 2

Predictive power of each prehypertension prediction model for men and women. Each graph presents the results for the LR, NB, and DT algorithms. (a,b) present the results for men, and (c,d) present the results for women. (a,c) show the results obtained with CFS, and (b,d) show the results obtained with WFS. Abbreviations: LR, logistic regression; NB, naïve Bayes; DT, decision tree; AUC, area under the receiver operating characteristic curve; PHTN, prehypertension; CFS, correlation-based Feature selection; WFS, wrapper-based feature selection.

Figure 3

Predictive power of each hypertension prediction model for men and women. Each graph presents the results for the LR, NB, and DT algorithms. (a,b) present the results for men, and (c,d) present the results for women. (a,c) show the results obtained with CFS, and (b,d) show the results obtained with WFS. Abbreviations: LR, logistic regression; NB, naïve Bayes; DT, decision tree; AUC, area under the receiver operating characteristic curve; HTN, hypertension; CFS, correlation-based feature selection; WFS, wrapper-based feature selection.