Prevalence and Associated Factors of Hypertension Among Adolescents: A Machine Learning Approach

Objective: This study aims to estimate the prevalence of HTN among high school students in Erbil City, Kurdistan Region, Iraq, and to identify associated factors using a machine learning approach. Methods: A school-based cross-sectional study was conducted (n = 1619). Eight distinct supervised learning algorithms included Logistic Regression (LR), k-Nearest Neighbors (kNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and Support Vector Machine (SVM), were implemented to compare the predictive performance. The performance of each model was assessed using the following metrics: Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PR), Balanced Accuracy, Precision, Recall, Specificity, and F1 score. The best-performing model was interpreted using SHapley Additive exPlanations. Results: The prevalence of HTN was 11.3% (95% CI: 9.8% - 13.0%). The highest (AUC-ROC) value (0.8306) was observed for the LR model. However, the XGBoost, RF, and GBM models exhibited slightly lower AUC-ROC values (0.8173, 0.8078, and 0.8041, respectively). Key factors for HTN prediction included higher salt intake, higher BMI, older age, higher sedentary behavior (hr/day), lower physical activity (days/week), positive family history of HTN, female sex, lower vegetable intake, lower sleep duration (hr/night), and lower physical activity (min/day). Conclusion: This study demonstrated that machine learning algorithms, particularly LR and XGBoost, provide high discriminative power for predicting adolescent hypertension. Future research should focus on validating these models across diverse geographic cohorts to ensure generalizability.