HRAESN-IoT: A Hybrid Residual Attention and Echo State Network Approach for IoT-Enabled Heart Disease Prediction

Detection of Ischemic Heart Disease needs immediate accurate identifications since incorrect medical assessments lead to serious outcomes. A perfect heart disease prediction model must combine deep learning techniques with the Internet of Things (IoT) for achieving diagnoses of high accuracy. The authors present HRAESN-IoT as a real-time IHD severity prediction method that retrieves patient data from wearable sensors enabled by Internet of Things technology. Using attention residual learning together with Echo State Network (ESN) the model discovers significant medical patterns along with maintaining stable learning for time-dependent predictions. HRAESN-IoT incorporates IoT technology to continuously monitor patients which leads to real-time detection of IHD severity especially for early diagnosis. The model achieves 97.2% accuracy when tested on the Kaggle cardiovascular illness dataset that contains 70,000 cases. This method delivers better results compared to existing models implying its capability to develop customized therapeutic plans and rapid cardiac disease detection in real-time.