IoT-Enabled Smart E-Healthcare System with Predictive Prescription Algorithm for Automatic Patient Monitoring and Treatment

In this research, an innovative IoT-enabled smart e-healthcare system for predictive healthcare and real-time patient monitoring is presented. To quickly obtain critical health metrics, the system makes use of a range of sensors, including ones that measure blood pressure, temperature, pulse rate, and oxygen levels. The sensor data is first carefully converted from analog to digital format using the Analog-to-Digital (A to D) converter, and then it is sent to the Raspberry Pi, which serves as the primary processing unit. Following processing, the data is moved to a cloud server, where it is stored securely and is only accessible by medical professionals. Machine learning models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), and Naive Bayes (NB) are used to predict health outcomes based on sensor readings in order to enhance the proactive capabilities of the system. The accuracy rate of 97.76% achieved by the ANN is comparatively higher. The results highlight the anticipatory healthcare's revolutionary potential—that is, its capacity to give precise and timely predictions to assist medical professionals.