MITIGATING COVID-19 TRANSMISSION IN SCHOOLS WITH DIGITAL CONTACT TRACING

Abstract

Technology advancements have a rapid
effect on every field of life, be it medical
field or any other field. Artificial
intelligence has shown the promising
results in health care through its decision
making by analysing the data. COVID19 has affected more than 100 countries
in a matter of no time. People all over
the world are vulnerable to its
consequences in future. It is imperative
to develop a control system that will
detect the coronavirus. One of the
solution to control the current havoc can
be the diagnosis of disease with the help
of various AI tools. In this paper, we
classified textual clinical reports into
four classes by using classical and
ensemble machine learning algorithms. 

Feature engineering was performed
using techniques like Term
frequency/inverse document frequency
(TF/IDF), Bag of words (BOW) and
report length. These features were
supplied to traditional and ensemble
machine learning classifiers. Logistic
regression and Multinomial Naı¨ve
Bayes showed better results than other
ML algorithms by having 96.2% testing
accuracy. In future recurrent neural
network can be used for better accuracy.