Cloud-Based Healthcare Risk Prediction And Cloud-Band Surgical Monitoring System Using Iot Devices

Abstract

The combination of IoT devices and cloud technology has changed the healthcare ecosystem, especially in surgical
environments, by providing real-time patient monitoring and risk prediction functionality. This research presents
a Cloud-Based Healthcare Risk Prediction and Cloud-Band Surgical Monitoring System that combines advanced
machine learning methods, cloud computing platforms, and IoT devices to monitor and predict risk levels for
patients in real time. The system combines Decision Trees for classifying patient risk levels, and a Gradient
Descent model-building process for optimizing model performance and accuracy levels. The paper highlights the
system's contributions to surgical decision-making, patient safety, and resource allocation. Results demonstrate
strong performance across the metrics of expected performance with an overall accuracy level of 93%. Challenges
remain with data security and privacy; however, the proposed system provides a scalable and reliable intervention
to predict patient risk and monitor surgical patients