IoT-Based Remote Patient Monitoring Systems: A Machine Learning Approach to Predictive Healthcare

Authors

  • Priyanka Merugu
  • A. C.Priya Ranjani
  • Rinisha K A
  • G. Yamini Satish
  • Bathila Prasanna Kumar

Keywords:

Remote Patient Monitoring, IoT in Healthcare, Predictive Analytics, Machine Learning, Wearable Devices, Health Monitoring, Smart Healthcare, Anomaly Detection, LSTM, Data-Driven Medicine

Abstract

Remote patient monitoring (RPM) has gained momentum with the proliferation of Internet of Things (IoT) devices and advancements in machine learning (ML). This research proposes an IoT-enabled RPM system integrated with ML models to enable early disease prediction and health trend analysis. The system collects real-time physiological data from wearable devices and environmental sensors and employs supervised learning algorithms for anomaly detection and risk classification. Our experiments conducted using a synthesized dataset simulating real-world vitals (e.g., heart rate, oxygen saturation, temperature), show that models like Random Forest and LSTM can predict critical health conditions with over 93% accuracy. This paper highlights the architecture, data pipeline, and predictive capabilities of the system, underscoring its potential in reducing hospital readmissions and enabling proactive healthcare

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Published

2025-06-02

How to Cite

1.
Merugu P, Ranjani AC, K A R, Satish GY, Kumar BP. IoT-Based Remote Patient Monitoring Systems: A Machine Learning Approach to Predictive Healthcare. J Neonatal Surg [Internet]. 2025Jun.2 [cited 2025Sep.13];14(30S):280-91. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/6949

Issue

Vol. 14 No. 30S (2025): Journal of Neonatal Surgery

Section

Original Article

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