Fog-Driven Heart Attack Prediction from Wearable Edge Devices

Cardiovascular diseases, such as heart attacks, are a significant global health concern, responsible for a great deal of annual mortality. The introduction of wearable edge devices with advanced sensors has enabled continuous realtime monitoring of physiological data. This research aims to use fog computing, a decentralized computing approach, to introduce a system for predicting heart attacks based on data from these wearable devices. The system features a custom Deep Learning (DL) model at the fog layer, allowing realtime analysis of health data without relying on centralized cloud processing. The model obtained an accuracy of 90% which is a significant improvement than the traditional models. This work contributes to the field of cloud computing in healthcare, showcasing the potential of fog-based solutions for timely and context-aware heart attack prediction. Successful outcomes could lead to scalable and decentralized healthcare systems, supporting proactive cardiac care and im-nrovina overall patient well-being,