IoT and XAI-driven data aggregation framework for intelligent decision-making in smart healthcare systems

The Internet of Things (IoT) is used in healthcare to monitor patients via wearable sensors to measure different physiological parameters. Smart healthcare IoT-enabled sensors and medical device data collaborate with other smart devices to transfer collected sensitive healthcare data to the central server in a secure manner. However, this collected data suffers from noise, imbalance, privacy concerns, and challenges in real-time analysis. Thus, this work is to develop a novel IoT and Explainable Artificial Intelligence (XAI) based data aggregation framework in smart healthcare systems to enable accurate patient health status and decision-making in real-time. Initially, body-integrated wearable sensors and devices collect physiological data, forming a comprehensive dataset. After that, this data is preprocessed and encrypted using Fully Homomorphic Encryption for secure transmission to the centralized servers. Meaningful features are extracted from the preprocessed data using Autoencoders, which perform effective dimensionality reduction while preserving critical information. Finally, Tabular Network (TabNet) classifies health status and risks with high precision. TabNet is a deep learning model specifically designed for structured data, which efficiently handles tabular data using attention mechanisms for feature selection and decision-making. The framework integrates XAI methods to provide interpretable predictions and actionable insights, ensuring transparency for healthcare providers. As a result, TabNet demonstrates a remarkable accuracy rate of 99.57 %, making it possible for doctors to provide consultations at any time, thereby improving the efficiency of traditional medical systems.