Machine Learning-Based IoT-Enabled Perspective Model for Prediction of COVID-19 Test in Early Stage

Abstract

COVID-19 is one of pandemic community disease. Cycle of COVID-19 is 1 to 14 days. The most widely recognised symptoms of COVID-19 are fever, dry Cough, difficulty in breathing and diarrhea. The safety of the health workers is an important issue. Equipments required for COVID-19 test and certified laboratories where tests are conducted are limited. This paper proposed a perspective model for the prediction of COVID-19 test at early stage based on symptoms. In this perspective model, input sounds of cough are recorded by Smart Voice Recorder and temperature by Bluetooth Thermometer. Classifiers such as random forest tree classifier, decision tree classifier can be used to predict the susceptible population for COVID -19 test on the basis of symptoms. Proposed perspective model will be helpful to reduce the human intervention and use of testing equipments for most infected population.