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Coronavirus (covid 19) is one of the most dangerous viruses that have spreadall over the world. With the increasing number of cases infected with thecoronavirus, it has become necessary to address this epidemic by all availablemeans. Detection of the covid-19 is currently one of the world's most difficultchallenges. Data science and machine learning (ML), for example, can aid in thebattle against this pandemic. Furthermore, various research published in thisdirection proves that ML techniques can identify illness and viral infectionsmore precisely, allowing patients' diseases to be detected at an earlier stage.In this paper, we will present how ontologies can aid in predicting thepresence of covid-19 based on symptoms. The integration of ontology and ML isachieved by implementing rules of the decision tree algorithm into ontologyreasoner. In addition, we compared the outcomes with various ML classificationsused to make predictions. The findings are assessed using performance measuresgenerated from the confusion matrix, such as F-measure, accuracy, precision,and recall. The ontology surpassed all ML algorithms with high accuracy valueof 97.4%, according to the results.

Integration of ontology with machine learning to predict the presence of covid-19 based on symptoms