title: Predicting increased blood pressure using machine learning.
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 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.
authors.affiliation: Laboratório de Investigação da Arquitetura Cognitiva, Universidade Federal de Minas Gerais, 30000-000 Belo Horizonte, Minas Gerais, MG, Brazil.
authors.forename: Hudson Fernandes
authors.initials: HF
authors.lastname: Golino
authors.affiliation: Instituto Multidisciplinar de Saúde, Universidade Federal da Bahia, 40000-000 Bahia, BA, Brazil.
authors.forename: Liliany Souza de Brito
authors.initials: LS
authors.lastname: Amaral
authors.affiliation: Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil.
authors.forename: Stenio Fernando Pimentel
authors.initials: SF
authors.lastname: Duarte
authors.affiliation: Laboratório de Investigação da Arquitetura Cognitiva, Universidade Federal de Minas Gerais, 30000-000 Belo Horizonte, Minas Gerais, MG, Brazil.
authors.forename: Cristiano Mauro Assis
authors.identifier: 0000-0003-3939-5807
authors.initials: CM
authors.lastname: Gomes
authors.affiliation: Instituto Multidisciplinar de Saúde, Universidade Federal da Bahia, 40000-000 Bahia, BA, Brazil.
authors.forename: Telma de Jesus
authors.initials: Tde J
authors.lastname: Soares
authors.affiliation: Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil.
authors.forename: Luciana Araujo
authors.initials: LA
authors.lastname: Dos Reis
authors.affiliation: Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil.
authors.forename: Joselito
authors.identifier: 0000-0001-6152-3903
authors.initials: J
authors.lastname: Santos
mesh_terms: D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D001794:Blood Pressure; D015992:Body Mass Index; D001837:Body Weights and Measures; D005260:Female; D006801:Humans; D006973:Hypertension / Q000209:etiology*; D007858:Learning; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009765:Obesity / Q000150:complications / Q000503:physiopathology*; D012737:Sex Factors; D055105:Waist Circumference; D049629:Waist-Hip Ratio; D055815:Young Adult