Journal's Facebook pageProbabilistic Reasoning for Diagnosis Prediction of Coronavirus Disease based on Probabilistic Ontology
- LRDSI Laboratory, Faculty of sciences, University Blida1
Soumaa, B.P 270, Blida, Algeria
{farehm, ishakriali, kherbachehaf, guemmouzm}@gmail.com
Abstract
The novel Coronavirus has been declared a pandemic by the World Health Organization (WHO). Predicting the diagnosis of COVID-19 is essential for disease cure and control. The paper’s main aim is to predict the COVID-19 diagnosis using probabilistic ontologies to address the randomness and incompleteness of knowledge. Our approach begins with constructing the entities, attributes, and relationships of COVID-19 ontology, by extracting symptoms and risk factors. The probabilistic components of COVID-19 ontology are developed by creating a Multi-Entity Bayesian Network, then determining its components, with the different nodes, as probability distribution linked to various nodes. We use probabilistic inference for predicting COVID-19 diagnosis, using the Situation-Specific Bayesian Network (SSBN). To validate the solution, an experimental study is conducted on real cases, comparing the results of existing machine learning methods, our solution presents an encouraging result and, therefore enables fast medical assistance.
Key words
COVID-19, Probabilistic Ontology, Multi-Entity Bayesian Networks, Uncertainty, Reasoning
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS220829035F
Publication information
Volume 20, Issue 3 (June 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
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How to cite
Fareh, M., Riali, I., Kherbache, H., Guemmouz, M.: Probabilistic Reasoning for Diagnosis Prediction of Coronavirus Disease based on Probabilistic Ontology. Computer Science and Information Systems, Vol. 20, No. 3, 1109–1132. (2023), https://doi.org/10.2298/CSIS220829035F
