Artificial Neural Network-Based Clinical Decision Support System for Personalized Hypertension Management

Hypertension is a complex medical condition that affects millions worldwide, making personalized management strategies essential for optimizing patient outcomes. This study presents a novel approach, leveraging Artificial Neural Networks (ANNs) to develop a Clinical Decision Support System (CDSS) tailored for hypertension management. The proposed methodology integrates extensive patient data, data preprocessing techniques, and genetic algorithm optimization to enhance the accuracy and precision of hypertension management. We compare the results of our approach with six traditional methods to highlight its superiority. The ANN s playa pivotal role in capturing intricate relationships within diverse patient data, resulting in personalized recommendations. Data preprocessing ensures data quality and relevance, while genetic algorithm optimization fine-tunes the network for improved performance. Our results showcase the proposed system's superiority in accuracy, sensitivity, specificity, precision, and F1 score, as well as in AUC-ROC and mean absolute error (MAE) for blood pressure prediction. It consistently outperforms traditional methods, emphasizing its potential in revolutionizing hypertension management. The proposed CDSS excels in personalizing care and addressing the complex, multifactorial nature of hypertension.