Citation :

Rejna Azeez Nazeema, Hind Salem Alatawi, Sameena Shaik, Sangeetha Komandur, Jayasuriya Panchalingam, Ragia Elsayed Eisawy Hussein, "A Hybrid AI IoT Framework for Real-Time Predictive Healthcare Analytics using Machine Learning," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 129-138, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P112

Abstract

Continuous patient monitoring and early risk prediction are two areas where the Internet of Things (IoT) and Artificial Intelligence (AI) might revolutionize healthcare. But there are still obstacles to overcome in order to provide precise, real-time analytics in contexts with limited resources. To do healthcare predictive analytics in real-time, this article introduces a unique hybrid AI-IoT framework that combines processing at the edge with Machine Learning (ML) in the cloud. Using low-power wearable IoT sensors, the system continually monitors vital indications like heart rate, blood pressure, temperature, and blood oxygen saturation. To keep latency and bandwidth consumption to a minimum, initial signal processing and anomaly detection are carried out at the edge using lightweight models. A weighted ensemble of Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks is used to provide advanced cloud-based predictive models for cardiovascular risk event forecasting. Using a public healthcare IoT dataset, the findings show that the proposed framework outperforms baseline models in terms of prediction accuracy, end-to-end latency reduction, and energy use optimization. A scalable approach for real-time remote health monitoring, the hybrid architecture successfully balances computational complexity with responsiveness.

Keywords

Artificial Intelligence, Internet Of Things, Predictive Healthcare Analytics, Machine Learning, Real-Time Monitoring, Edge Computing.

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