IoT-Based Remote Patient Monitoring Systems: A Machine Learning Approach to Predictive Healthcare - Merugu, Priyanka; Ranjani, A. C.Priya; K A, Rinisha; Satish, G. Yamini; Kumar, Bathila Prasanna (Journal of Neonatal Surgery, 2025)
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