An IoT-based system for effective COVID patient health monitoring with SVM decision making

ABSTRACT

Coronavirus disease (COVID-19) epidemic has exaggerated billions of persons, where most of them have lost their survives. Self-quarantine and continuous monitoring is the primary solution to avoid spreading and death rate. Internet of Things (IoT) development takes new chances in many applications, like smart cities etc . IoT combined with machine learning offers a hopeful solution for continuous patient monitoring with alert. In this work, IoT based covid patient health monitoring system introduced using the Arduino controller. The proposed Arduino based system consists of a pulse sensor, oximeter and temperature sensor. In addition, the machine learning algorithm of Support Vector Machines or SVM used to predict or alert about health risk conditions of a patient. SVM model trained using data set collected from world health organization for various age patients. Implementations results prove that the proposed system achieves higher classification accuracy with minimum cost expenses.