Integration of ontology with machine learning to predict the presence of covid-19 based on symptoms
This work addresses the challenge of early COVID-19 detection for healthcare applications, but it appears incremental as it combines existing methods (ontology and decision trees) without introducing a fundamentally new approach.
The paper tackles the problem of detecting COVID-19 by integrating ontology with machine learning to predict its presence based on symptoms, achieving a high accuracy of 97.4% that surpassed other ML algorithms.
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.