Key result
The proposed hybrid CNN-BiLSTM model, integrated with a Deep Q-Network for dynamic resource allocation, achieved an accuracy of 98.35% in predicting heart attacks from physiological data.
Why the study?
The rapid growth of real-time health data presents major challenges in resource allocation and processing for cardiac event prediction in healthcare systems.
Does a hybrid CNN-BiLSTM model combined with Deep Q-Network (DQN) optimize real-time cardiac event prediction and resource allocation in an edge-fog computing environment?
Does a hybrid CNN-BiLSTM model combined with Deep Q-Network (DQN) optimize real-time cardiac event prediction and resource allocation in an edge-fog computing environment?
An AI-driven fog-edge computing architecture combining CNN-BiLSTM and DQN achieves 98% accuracy for cardiac event prediction while optimizing real-time resource allocation.
Promising accuracy in heart attack prediction warrants prospective validation; leaves open clinical utility and integration with dynamic resource allocation.
The evolution of healthcare, driven by remote monitoring and connected devices, is transforming medical service de-livery. Digital twins, virtual replicas of patients, enable continuous monitoring and predictive analysis. However, the rapid growth of real-time health data presents major challenges in resource allocation and processing, especially in cardiac event prediction scenarios. This paper proposes an artificial intelligence-based approach to optimize resource allocation in a fog-edge computing environment, with a focus on Mauritania. The system integrates a deep learning model (CNN-BiLSTM), which achieves 98%accuracy in predicting cardiovascular risks from physiological signals, combined with a Deep Q-Network (DQN) to dynamically decide whether tasks should run at the edge or in the fog. Using IoT sensors, real-time health data is collected and processed intelligently, ensuring low latency and rapid response. Digital twins provide a synchronized virtual representation of the physical system for real-time supervision. This architecture improves resource utilization, reduces processing delays, and enhances responsiveness to critical medical conditions, supporting more accurate cardiac event prediction and timely intervention, especially in resource-constrained environments.
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Nouh et al. (2025) studied Cardiovascular disease (n=303). Hybrid CNN-BiLSTM model with Deep Q-Network (DQN) vs. Existing predictive models (literature comparison) was evaluated on Prediction accuracy. The proposed hybrid CNN-BiLSTM model, integrated with a Deep Q-Network for dynamic resource allocation, achieved an accuracy of 98.35% in predicting heart attacks from physiological data.
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