A custom Deep Learning model deployed at the fog layer for real-time heart attack prediction from wearable edge devices achieved an accuracy of 90%, improving upon traditional models.
Does a fog computing-based deep learning system using wearable device data improve heart attack prediction accuracy compared to traditional models?
A fog computing-based deep learning system using wearable sensor data achieved 90% accuracy in predicting heart attacks, offering a decentralized approach for real-time cardiac monitoring.
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,
Ravindranathan et al. (Sat,) conducted a other in Heart attacks. Fog computing-based Deep Learning model vs. Traditional models was evaluated on Prediction accuracy. A custom Deep Learning model deployed at the fog layer for real-time heart attack prediction from wearable edge devices achieved an accuracy of 90%, improving upon traditional models.