Anomaly based threat detection in smart health using machine learning

The Anomaly Based Threat Detection in Smart Health Using Machine Learning system successfully provides an intelligent and secure solution for monitoring healthcare data and detecting abnormal activities. By utilizing the Isolation Forest algorithm, the system can identify anomalies efficiently and generate timely alerts for healthcare professionals. Features such as OTP verification, role-based access control, patient record management, and anomaly logging enhance both security and reliability. The developed system reduces manual monitoring efforts, improves patient safety, and supports better healthcare decision-making. Overall, the project demonstrates the effective application of machine learning techniques in creating a secure, reliable, and efficient smart healthcare threat detection system.

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