Prediction of Respiratory Tract Infections Using IoT and RNN Techniques

Authors

  • Latika Pinjarkar Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), India
  • S. Sagayamary Department of ECE, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
  • Rekha P. Department of ECE, BNM Institute of Technology, Bangalore, Karnataka, India
  • Sivaprasad Lebaka Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  • Porandla Srinivas Department of IT, Malla Reddy Institute of Engineering and Technology, Hyderabad, Telangana, India
  • Rajendar Sandiri Department of ECE, Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India
  • Jayabharathi Ramasamy Department of CSE, RMK College of Engineering and Technology, Chennai, Tamil Nadu, India
  • Srinivasan C. Department of CSE, Saveetha School of Engineering, Saveetha University, Chennai, Tamil Nadu, India
Volume: 15 | Issue: 5 | Pages: 27250-27256 | October 2025 | https://doi.org/10.48084/etasr.11642

Received: 23 April 2025 | Revised: 20 May 2025 | Accepted: 6 June 2025 | Online: 6 October 2025


Corresponding author: Srinivasan C.

Abstract

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.

Keywords:

respiratory tract infections, pattern recognition, predictive analytics, health monitoring

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How to Cite

[1]
L. Pinjarkar, “Prediction of Respiratory Tract Infections Using IoT and RNN Techniques”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 27250–27256, Oct. 2025.

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Copyright (c) 2025 Latika Pinjarkar, S. Sagayamary, Rekha P., Sivaprasad Lebaka, Porandla Srinivas, Rajendar Sandiri, Jayabharathi Ramasamy, Srinivasan C.

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