<![CDATA[Hypertension is a chronic medical condition that, if undetected early, can result in life-threatening complications such as cardiovascular disease and stroke. Despite numerous studies on predictive modeling for hypertension, existing approaches often suffer from limited accuracy due to suboptimal feature selection, inadequate hyperparameter tuning, and imbalanced datasets. This study aims to address these limitations by proposing a hybrid deep learning framework that integrates Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models with Ant Colony Optimization (ACO) for feature selection and Bayesian Optimization (BO) for hyperparameter tuning. The proposed method is trained on Electronic Health Records (EHR) and employs the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance. Experimental results show that the optimized ANN achieves an accuracy of 94.3% and the optimized LSTM reaches 95.1%, outperforming baseline models without optimization. Improvements in precision, recall, and F1-score further demonstrate the model’s robustness in identifying hypertension cases. The main contribution of this research lies in the integration of ACO-based feature optimization and BO-based hyperparameter tuning within a hybrid ANN–LSTM framework, resulting in a clinically applicable model for early hypertension prediction. These findings suggest that the proposed approach has strong potential for deployment in electronic medical record systems to support timely and accurate clinical decision-making.]]>
Copyrights © 2026
