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Open AccessArticle

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

by 1 and 2,3,*
1
Database/Bioinformatics Laboratory, Chungbuk National University, Cheongju 28644, Korea
2
Faculty of Information Technology, Ton Duc Thang University, Hochiminh City 700000, Vietnam
3
Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(11), 2571; https://doi.org/10.3390/ijerph15112571
Received: 24 October 2018 / Revised: 8 November 2018 / Accepted: 14 November 2018 / Published: 16 November 2018
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. View Full-Text
Keywords: machine learning; feature selection; hypertension; prehypertension; anthropometry; spirometry machine learning; feature selection; hypertension; prehypertension; anthropometry; spirometry
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MDPI and ACS Style

Heo, B.M.; Ryu, K.H. Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry. Int. J. Environ. Res. Public Health 2018, 15, 2571. https://doi.org/10.3390/ijerph15112571

AMA Style

Heo BM, Ryu KH. Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry. International Journal of Environmental Research and Public Health. 2018; 15(11):2571. https://doi.org/10.3390/ijerph15112571

Chicago/Turabian Style

Heo, Byeong M.; Ryu, Keun H. 2018. "Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry" Int. J. Environ. Res. Public Health 15, no. 11: 2571. https://doi.org/10.3390/ijerph15112571

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