ABSTRACT Objective: To evaluate and to compare machine learning models for predicting hypertension in patients with diabetes using routine clinical variables. Methods: Using Behavioral Risk Factor Surveillance System data, models were trained on 35,346 individuals with seven variables (“HighChol”, “BMI”, “Smoker”, “PhysActivity”, “Sex”, and “Age”) to predict the occurrence of hypertension in patients with diabetes (“HTNinDM”). Models included neural network, gradient boosting, random forest, Adaptive Boosting, and logistic regression. Performance was assessed by area under the curve, accuracy, precision, and […]
