Digital Twin-Enabled Intelligent IoT Healthcare Systems

The increasing adoption of Internet of Things (IoT) devices in healthcare has led to the emergence of Digital Twin technologies as a revolutionary approach to enhance healthcare monitoring and decision-making. In this paper, we present a hybrid ML model combining CNNs and LSTM networks with an attention mechanism to process IoT healthcare data. Using a real-world IoT dataset, we developed a framework capable of predicting device capacity and identifying anomalies. The proposed model achieved a MAE of 0.0896 and an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.6261, outperforming existing models. This study demonstrates the potential of digital twins in creating context-aware intelligent systems for personalized healthcare monitoring and diagnostics.