Decision tree algorithms effectively predicted incident hypertension with an area under the curve of 0.78 in the total population, identifying systolic blood pressure and age as the primary predictors.
Cohort (n=6,205)
No
Do decision tree algorithms accurately predict incident hypertension in adults without baseline cardiovascular disease?
Decision tree algorithms using easily available variables like systolic blood pressure, age, and waist circumference can effectively predict incident hypertension and identify high-risk subgroups.
Effect estimate: AUC 0.78 (95% CI 0.75-0.80)
Hypertension is a critical public health concern worldwide. Identification of risk factors using traditional multivariable models has been a field of active research. The present study was undertaken to identify risk patterns associated with hypertension incidence using data mining methods in a cohort of Iranian adult population.Data on 6205 participants (44% men) age > 20 years, free from hypertension at baseline with no history of cardiovascular disease, were used to develop a series of prediction models by 3 types of decision tree (DT) algorithms. The performances of all classifiers were evaluated on the testing data set.The Quick Unbiased Efficient Statistical Tree algorithm among men and women and Classification and Regression Tree among the total population had the best performance. The C-statistic and sensitivity for the prediction models were (0.70 and 71%) in men, (0.79 and 71%) in women, and (0.78 and 72%) in total population, respectively. In DT models, systolic blood pressure (SBP), diastolic blood pressure, age, and waist circumference significantly contributed to the risk of incident hypertension in both genders and total population, wrist circumference and 2-h postchallenge plasma glucose among women and fasting plasma glucose among men. In men, the highest hypertension risk was seen in those with SBP > 115 mm Hg and age > 30 years. In women those with SBP > 114 mm Hg and age > 33 years had the highest risk for hypertension. For the total population, higher risk was observed in those with SBP > 114 mm Hg and age > 38 years.Our study emphasizes the utility of DTs for prediction of hypertension and exploring interaction between predictors. DT models used the easily available variables to identify homogeneous subgroups with different risk pattern for the hypertension.
Ramezankhani et al. (Mon,) conducted a cohort in Hypertension (n=6,205). Decision tree algorithms (CART, QUEST, C5.0) was evaluated on Prediction of incident hypertension (AUC for CART model in total population) (AUC 0.78, 95% CI 0.75-0.80). Decision tree algorithms effectively predicted incident hypertension with an area under the curve of 0.78 in the total population, identifying systolic blood pressure and age as the primary predictors.