  Coronavirus (covid 19) is one of the most dangerous viruses that have spread
all over the world. With the increasing number of cases infected with the
coronavirus, it has become necessary to address this epidemic by all available
means. Detection of the covid-19 is currently one of the world's most difficult
challenges. Data science and machine learning (ML), for example, can aid in the
battle against this pandemic. Furthermore, various research published in this
direction proves that ML techniques can identify illness and viral infections
more precisely, allowing patients' diseases to be detected at an earlier stage.
In this paper, we will present how ontologies can aid in predicting the
presence of covid-19 based on symptoms. The integration of ontology and ML is
achieved by implementing rules of the decision tree algorithm into ontology
reasoner. In addition, we compared the outcomes with various ML classifications
used to make predictions. The findings are assessed using performance measures
generated from the confusion matrix, such as F-measure, accuracy, precision,
and recall. The ontology surpassed all ML algorithms with high accuracy value
of 97.4%, according to the results.
