A classification tree model using anthropometric measures outperformed traditional logistic regression in predicting increased blood pressure, achieving a pseudo R2 of 0.43 in women and 0.46 in men.
Observational (n=400)
Does a machine learning classification tree using anthropometric measures improve the prediction of increased blood pressure compared to traditional logistic regression in college students?
Machine learning classification trees using simple anthropometric measures (BMI, waist and hip circumference) can predict increased blood pressure more effectively than traditional logistic regression.
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.
Golino et al. (Wed,) conducted a observational in Increased blood pressure (n=400). Classification tree machine learning vs. Traditional logistic regression was evaluated on Prediction of increased blood pressure. A classification tree model using anthropometric measures outperformed traditional logistic regression in predicting increased blood pressure, achieving a pseudo R2 of 0.43 in women and 0.46 in men.