<scp>VahigoNet</scp> : Leveraging Deep Learning for Transparent and High‐Performance Hypertension Prediction

ABSTRACT Hypertension continues to be a primary cause of global death, necessitating early and accurate forecasting for effective treatments. The existing methods have drawbacks such as class imbalance, poor modeling of sequential and spatial connections, high computation costs, and lack of interpretability, even though Deep Learning (DL) models offer possible solutions. To tackle these difficulties, we present VahigoNet, a novel blending DL model that incorporates vanilla recurrent neural networks (VRNN) for capturing temporal correlations, Google network for extracting hierarchical spatial features, and highway networks (HighwayNet) for adaptive feature refinements. To achieve strong generalization, we utilize the synthetic minority oversampling technique (SMOTE) for data balance. VahigoNet substantially outperforms baseline models, showing enhancements of 9.39% in accuracy, 10.27% in precision, 8.63% in recall, 9.39% in F1‐score, and 3.10% in area under the curve‐receiver operating characteristic. A 10‐fold cross validation method is utilized to assess the model's generalizability, markedly reducing overfitting and improving robustness. A paired t ‐test is performed to evaluate statistical significance, demonstrating that the enhancements are substantial and clinically relevant. Additionally, explainable artificial intelligence (AI) methodologies, including local Interpretable model‐agnostic explanations (LIME) and SHapley Additive exPlanations, are incorporated to provide both local and global perspectives on feature contributions. These explainability strategies enhance transparency, making VahigoNet a more interpretable and clinically reliable model for hypertension prediction. The results demonstrate that VahigoNet is an exceptionally efficient and transparent method, achieving a balance between predictive capability and practical relevance in medical diagnostics.