<p>Evaluation metrics of ML and DL models.</p>

Among the machine learning and deep learning models, weighted logistic regression (WLR) achieved the highest accuracy (0.817), precision (0.444), specificity (0.981), AUC-ROC (0.751), and AUC-PR (0.357). However, WLR exhibited low recall (0.070). In contrast, the random forest (RF) model achieved the highest recall (0.687) and F1-score (0.460) on the test data, indicating greater sensitivity in identifying individuals with hypertension.

Additionally, age, BMI, sex, family size, and educational level were identified as the most important predictors among the variables included in the study.</p> <p>Conclusion</p><p>Hypertension is common in Bangladesh, with higher prevalence in women and significant association with socio-demographic determinants. Although WLR demonstrated the highest accuracy, precision, specificity, and AUC-PR, its low recall limits its utility for identifying individuals with hypertension.

RF may be more suitable for public health applications because of its higher recall and F1-score; however, further external validation and assessment of its clinical utility are required before implementation.</p></div>