Hybrid Anomaly Detection Model Integrating Lstm and Isolation Forest for Enhanced Performance in Wireless Sensor Networks

An importance of timely detection of anomalies not only for patient welfare but also operational success, the study examines how effective real time anomaly detection can be achieved in Wireless Sensor Networks (WSNs) in health care environments. By focusing on the development of a platform that combines ensemble methods and various deep learning capabilities dedicated to learning various forms of anomalies within a new integration of Long Short-Term Memory (LSTM) network systems within the base of the platform as it combines the inherent deep learning capabilities of the LSTM with various components of the Isolation Forest which helps identify various outliers, the platform focuses on the ability of the LSTM to learn and map out anomalies through the draw features of an Isolation Forest that are able to identify items that are outliers and provide a significant level of efficiency within the detection of anomalies. In laboratory evidence, the hybrid system yields far better results as compared to the standard detection procedures with more than 20 % improvement in accuracy and much reduced false-positive rates. We demonstrate the proposed model has the capability to analyze historical sensor data, so that it can recognize anomalies effectively over certain period of time. The medical benefits of monitoring technologies are fast healthcare response which leads to improved patient health outcomes. Recent studies in anomaly detection in Wireless Sensor Networks lead to important and practical implications for data management systems in intelligent healthcare technology which helps in improving the efficacy and safety of Medical services.