Аңдатпа
The demand for an effective, accessible, and eventual personalized healthcare brings Machine Learning (ML) and Internet-of-Things (IoT) together; and now, these extreme revolutions support smart and dynamic healthcare through realtime personalized medical systems. PhysioData continues to stream in from wearables and other embedded sensor devices that are always on, constantly enabled by IoT-based smart healthcare systems. That builds enormous big data ready for the next level of ML analysis, automatically predicting disease conditions detected as anomalies within patient monitoring, addressing one of the most critical clinical problems, resource allocation, being an urgent indication automatically diagnosed earlier. This paper presents an original experimental study on privacy-preserving federated Deep Learning (DL) for IoT-based multimodal cardiac monitoring. The approaches reviewed make diagnoses more accurate by reducing delay and improving data privacy in distributed environments. Therefore, these approaches are suitable to real-world scenarios. Current models show that ML-enabled IoT architectures are able to provide secure, reliable and scalable solutions. The available evidence indicates that ML and IoT technologies enable predictive analytics, fast decision-making, and adaptive health management, which leads to better health outcomes via improved efficiency. Hence, this would be something totally new for patient care if MLIoT tech can deliver what it promises.