The convergence of Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) has transformed modern bioanalytical practices, enabling real-time physiological data acquisition, transmission, and analysis. This paper proposes a secure, scalable, and intelligent bioanalytical framework—BioCloudSense—which integrates IoT-based biosensors, cloud-based data processing, and deep learning models for early detection of critical health conditions. The system collects multi-modal physiological data (ECG, SpO₂, temperature, etc.) from wearable devices and transmits it securely to a federated cloud platform, where pre-trained convolutional and recurrent neural networks perform real-time diagnostic predictions. Differential privacy and blockchain-based identity management secure patient data during cloud processing. Experimental validation on the MIT-BIH and MIMIC-III datasets demonstrates 97.3% accuracy for arrhythmia detection and 94.1% precision in early sepsis prediction. The proposed framework offers a robust architecture for real-time, remote bioanalysis, ensuring high diagnostic accuracy while preserving privacy and scalability.
Preethi Madadi (Thu,) studied this question.