Real-Time Health Monitoring Using IoT Sensors and Predictive Machine Learning Models

Abstract

The convergence of Internet of Things (IoT) and Machine Learning (ML) technologies at high speed has made it possible to revolutionize the delivery of healthcare from episodic to continuous and predictive care. This research proposes a holistic IoT-ML framework for real-time health monitoring for early detection of physiological abnormalities like tachycardia, hypoxemia, and fever. The main goal was to design and test a scalable system combining IoT-based wearable sensors and predictive ML models Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)—to predict normal and abnormal health conditions. The approach consisted of gathering a dataset of 15,000 labeled physiological signals and experimental deployment on real-time streams of 10 human subjects, with a separate validation set of 500 live sensor samples. Preprocessing involved signal denoising, normalization, and feature extraction in the time and frequency domains. The LSTM model showed better performance with accuracy of 96.8% and AUC of 0.98, while GBM and RF provided an optimal balance of efficiency and interpretability. Real-time alert generation using clinical thresholds provided precision of 95% with mean latency of 1.92 seconds. The proposed framework was concluded to be able to efficiently fill the current gaps like high false alert rates and no real-time adaptability. It provides a strong, interpretable, and deployable approach for both clinical and remote healthcare environments, greatly moving the current state of AI-augmented personalized medicine by virtue of its multi-model benchmarking, real-time validation, and deployment feasibility evaluation.