Integrated IoT-Enabled Smart E-Healthcare System for Automatic Patient Monitoring and Predicting Treatment Using RaspberryPi

This study presents the development of an IoT-enabled smart e-healthcare system that utilizes a Raspberry Pi for real-time patient monitoring and predictive treatment recommendations using a Random Forest algorithm. The system integrates multiple sensors, including heart rate, temperature, respiratory, and pulse oximeter, to continuously track vital signs. The collected data is processed through an ADC module and analyzed by the Raspberry Pi to detect potential health issues and suggest appropriate treatments. An LCD provides real-time status updates, while IoT connectivity ensures remote access to patient data. By leveraging predictive analytics, this system enables proactive healthcare interventions, improving patient outcomes and reducing the risk of medical emergencies.