Prediction of Respiratory Tract Infections Using IoT and RNN Techniques

This research examines the efficacy of an IoT-based system using Recurrent Neural Networks (RNNs) for the early identification and short-term prognosis of Respiratory Tract Infections (RTIs). The proposed system uses simulated real-time physiological data (respiratory rate, heart rate, temperature, oxygen saturation, and white blood cell count) from the MIMIC-III dataset to emulate IoT sensor outputs, achieving 92.1% classification accuracy. The findings highlight the efficacy of integrating continuous monitoring principles with advanced temporal modeling for proactive healthcare treatment. The novelty of this work lies in the use of LSTM-based RNNs with simulated multi-parameter IoT data for early RTI identification. This approach outperforms the traditional static models by effectively capturing the temporal dependencies in the physiological signals of Intensive Care Unit (ICU) patients.