An Explainable IoT-based Framework for Anomaly Detection and Emergency Decision Management in Smart Healthcare

As the usage of IoT technology in health care has grown, patient monitoring in real time has become more feasible. However, existing systems do not usually possess accurate anomaly detection, timely emergency response, and interpretable decision support. To address these limitations, this paper proposes a Decision-Integrated Anomaly Detection and Emergency Management framework with eXplainable models (DIADEM-X). DIADEM-X employs physiological data from IoT sensors to identify unusual health patterns using an XGBoost classifier augmented with SHapley Additive exPlanations (SHAP) explanations for clinical interpretability. An innovative Emergency Decision Fusion Engine combines several sensor readings and patient history to create priority-ordered alerts, reducing false alarms and alert fatigue. For deployment on the edge, the system uses low-latency in constrained resources to make it deployable in hospital wards, home care, and mobile units of health. Comparison with SVM, CNN, and Bi-LSTM models shows that DIADEM-X reports 96.1% accuracy, 94 F1-score, 370 ms alert latency, and a 3.5% false alarm rate, which is a considerable improvement over current method. The system's reliability, speed, and explainability make it a potential candidate as a next-generation intelligent health care monitoring system. In this paper, the author identifies the possibilities of using machine learning, explainable AI, and edge computing to achieve trusted, efficient, and clinically relevant IoT-based health systems.