Probabilistic Reasoning for Diagnosis Prediction of Coronavirus Disease based on Probabilistic Ontology

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.

Automated summary

The World Health Organization (WHO) has declared the novel Coronavirus a pandemic. Predicting the diagnosis of COVID-19 is crucial for disease control and treatment. This paper proposes an approach that utilizes probabilistic ontologies to address the uncertainty and incompleteness of knowledge, aiming to predict the COVID-19 diagnosis. By constructing the entities, attributes, and relationships of COVID-19 ontology, probabilistic components are developed using a Multi-Entity Bayesian Network. The results show promising potential for fast medical assistance.