Survey on Advanced Techniques for Cardiac Arrest Prediction Using Machine Learning

Cardiac arrest has been a leading cause of mortality worldwide, with limited opportunities for intervention. This project introduces a novel machine-learning approach to predict and process cardiac arrest risk in high-risk patients. Our system integrates 1. Advanced signal processing techniques for ECG analysis 2. Deep learning algorithms for pattern recognition 3. Real-time monitoring and alert system. By analyzing ECG data, medical history, and vital signs, our system provides personalized risk scores, enabling early intervention and improving patient outcomes. Our approach has the potential to revolutionize cardiac arrest prediction, saving lives and reducing healthcare costs.