##The AI-Driven Healthcare Anomaly Detection System is an end-to-end real-time monitoring and decision-support platform designed to detect abnormal patterns in patient vital signs and generate early health risk alerts.
##Unlike traditional healthcare monitoring systems that rely on static thresholds or post-incident analysis, this system continuously analyzes live physiological data streams using machine learning models to identify anomalies as they occur.
##Detected anomalies are stored in a PostgreSQL database, visualized on an interactive Flask-based dashboard, and critical alerts automatically trigger email notifications to healthcare administrators.
##The project demonstrates the practical application of machine learning, real-time data streaming, backend APIs, and dashboard visualization in solving real-world healthcare monitoring challenges.