Real-Time Anomaly Detection in IoT Healthcare Devices With LSTM

In this study, LSTM-based models are used to investigate real-time anomaly detection in IoT healthcare equipment. The study demonstrates the way these models are incredibly successful in improving patient outcomes and data security. The results regularly show great memory, and accuracy, alongside precision, underscoring their suitability for critical care situations. While acknowledging the importance of this accomplishment, the research also points out some possible drawbacks, such as the reliance on historical data and the requirement for more model scalability and interpretability research. Recommendations cover practical implementations, cutting-edge data security safeguards, and thorough standards. Model optimization for resource-constrained IoT devices, and edge computing, as well as improved model interpretability through comprehensibility approaches and federated learning should be prioritized in future development.