| Autor: |
Golino HF; Laboratório de Investigação da Arquitetura Cognitiva, Universidade Federal de Minas Gerais, 30000-000 Belo Horizonte, Minas Gerais, MG, Brazil., Amaral LS; Instituto Multidisciplinar de Saúde, Universidade Federal da Bahia, 40000-000 Bahia, BA, Brazil., Duarte SF; Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil., Gomes CM; Laboratório de Investigação da Arquitetura Cognitiva, Universidade Federal de Minas Gerais, 30000-000 Belo Horizonte, Minas Gerais, MG, Brazil., Soares Tde J; Instituto Multidisciplinar de Saúde, Universidade Federal da Bahia, 40000-000 Bahia, BA, Brazil., Dos Reis LA; Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil., Santos J; Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil. |
| Abstrakt: |
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 R (2) (.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 R (2) (.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. |
