Real-Time Detection and Monitoring of Contagious Diseases Using Wearable Sensors and Lightweight Model in Edge Networks

Infectious/Contagious diseases remain a significant global health challenge, necessitating accurate identification to mitigate their spread. The widespread adoption of wearable healthcare devices capable of continuously monitoring physiological parameters, presents a unique opportunity for enhancing disease detection strategies. In this paper, we develop a new Data Fusion-enabled Explainable Artificial Intelligence-assisted Light-GBM (FuXAI) model in edge networks to predict contagious disease in the early stage. The overall contributions of the proposed FuXAI model are three-fold. Firstly, we create a comprehensive health profile for each individual by employing data fusion techniques and integrating health parameters received from multiple sources. Secondly, we integrate a new lightweight Machine Learning (ML) model, the Light Gradient Boosting Model (LightGBM), to predict the disease at resource-constraint edge devices over the fused data. Finally, we leverage the power of Explainable Artificial Intelligence to develop interpretable algorithms, integrating with the LightGBM that can identify subtle patterns and correlations within the fused data, potentially revealing early warning signs of infectious diseases and creating trust in medical professionals during decision-making. Extensive simulation results of the proposed model over the standard ML models using benchmark datasets demonstrate the effectiveness of the model.