Comparative machine learning models for hypertension prediction in a cohort of patients with diabetes using routine...

To evaluate and to compare machine learning models for predicting hypertension in patients with diabetes using routine clinical variables.

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 recall, and F1 score using cross-validation. Class imbalance was addressed via diverse models. Feature importance was evaluated by permutation importance of a random forest model.

The neural network model achieved the best performance with area under the curve 0.689, accuracy 76.5%, precision 76.3%, recall 98.8%. Gradient boosting models performed similarly. Age and body mass index were the top predictors.

Machine learning models show potential for identifying patients with diabetes at high hypertension risk using routine clinical data. A neural network model achieved excellent predictive performance.