An IoT-based system for health monitoring and surveillance in post-covid life.

Internet of Things (IoT)-based health monitoring devices may be very helpful for COVID-19 pandemic patients. The main goal of patient monitoring in post-covid life is to have a quantitative assessment of the vital physiological markers of patients. An IoT-based health monitoring and surveillance system is presented in this paper after the COVID pandemic. This device monitors a patient's oxygen saturation level, heart rate, temperature, pulse rate, and systolic and diastolic blood pressure. New evidence suggests that ML and AI approaches could considerably mitigate the viral attack. In this article, we establish a collaboration between an IoT system and a machine learning prediction model to keep tabs on people's health in the post-COVID age. The algorithm's severity forecasts for patients were very accurate, taking into account a number of criteria including laboratory data and vital signs. The data was analysed using two classification techniques: random forest (RF) and support vector machine (SVM). Multiple techniques were used to pre-process the data. Outperforming the SVM, RF achieved a score of 0.956. [ABSTRACT FROM AUTHOR]