Synaptic Prognosticator: Harnessing Deep Learning for Stroke Anticipation

Stroke remains a global concern necessitating early detection for effective intervention. Our proposal introduces a novel approach to early stroke risk prediction using sensor-equipped smart gloves integrated with deep learning algorithms. The gloves are designed to continuously track vital physiological data, such as hand movements, grip strength, and cardiovascular indicators. By employing advanced deep learning models including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and attention mechanisms, the system processes the gathered data to detect patterns linked to stroke risk. The predictive model also incorporates additional patient data to estimate stroke probability. A real-time alert system notifies health care professionals of increased stroke risk, facilitating prompt intervention and preventive strategies. This solution combines wearable technology with cutting-edge analytics to improve stroke prediction, reduce the risk of severe outcomes, and enhance overall patient care, marking a significant step forward in proactive health management.