Anomaly Detection in IoT-Connected Medical Devices Using Machine Learning for Disease Monitoring

More and more, the Internet of Things (IoT) is being used in healthcare. This has made disease tracking much better by letting medical gadgets and apps send data in real time. But it's still hard to make sure that this data is correct and complete, especially when there are oddities that can happen because of broken devices, online threats, or strange physical situations. This paper discusses a machine learning approach for locating unusual items in IoT-connected medical equipment. The aim is to increase the dependability of disease surveillance systems. The proposed approach finds unusual patterns in streams of body data distinct from one another using unsupervised and semi-supervised learning models such as Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks. The system architecture is suitable for real-time healthcare applications as it can be implemented on edge, fog, and cloud platforms. With an F1-score of 0.86 and an AUC of 0.91, the LSTM model was the most accurate based on testing utilising both fake and actual datasets. It outperformed conventional techniques such as k-means clustering and Z-score. Two graphical techniques that indicate how well the intended system functions are ECG anomaly detection plots and ROC curves. A flexible and explainable machine learning process, context-aware anomaly scores with EHR integration, and new ideas about how to make models more general and how to balance computing needs are some of the most important advances. These results show that intelligent anomaly detection systems can help with early action, cut down on fake alarms, and make smart healthcare settings safer for patients.