IOT-ENABLED MACHINE LEARNING FRAMEWORK FOR REAL-TIME CARDIAC ANOMALY PREDICTION AND EMERGENCY ALERTING

IoT-Enabled Machine Learning Framework for Real-Time Cardiac Anomaly Prediction and Emergency Alerting is the proposed invention. The proposed invention
integrates wearable biomedical sensors with IoT connectivity to continuously collect cardiac physiological signals such as ECG and heart rate data. Advanced machine
learning and deep learning algorithms analyze the real-time data to detect abnormal cardiac patterns with high accuracy and minimal latency using edge and cloud
computing. Upon identification of a critical cardiac anomaly, the system automatically generates emergency alerts and transmits them to patients, caregivers, and
medical professionals along with essential health information. The framework also incorporates secure data transmission and privacy-preserving mechanisms, enabling
reliable, scalable, and intelligent remote cardiac monitoring for proactive healthcare and timely emergency response.