Men's dataset from the "Predicting increased blood pressure using Machine Learning" paper

<p>This dataset was part of a study that investigated the prediction of increased blood pressure (systolic blood pressure > 120 mmHg for women, and systolic blood pressure > 139 mmHg for men) 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 (Mean = 23.14, Standard Deviation = 6.03). The sample was divided into two sets of each sex (training and test) for cross-validation. 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 is the combination that produces the best prediction, since it has the lowest deviance (87.42) and misclassification (.19), and the higher pseudo R2 (.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) and misclassification (.16), and the higher pseudo R2 (.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. The new prediction algorithms provided by the current paper can be used to estimate increased blood pressure in women and hypertension in men. This can be of special interest for health professionals that have scarce material resources (such as blood pressure monitors), and that need to rely on inexpensive and easy to use diagnostic methods.