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Journal of Obesity
Volume 2014 (2014), Article ID 637635, 12 pages
http://dx.doi.org/10.1155/2014/637635
Research Article

Predicting Increased Blood Pressure Using Machine Learning

1Laboratório de Investigação da Arquitetura Cognitiva, Universidade Federal de Minas Gerais, 30000-000 Belo Horizonte, Minas Gerais, MG, Brazil
2Instituto Multidisciplinar de Saúde, Universidade Federal da Bahia, 40000-000 Bahia, BA, Brazil
3Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil

Received 16 August 2013; Revised 12 October 2013; Accepted 16 November 2013; Published 23 January 2014

Academic Editor: Yuichiro Yano

Copyright © 2014 Hudson Fernandes Golino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.