Forging An Optimized Bayesian Network Model With Selected Parameters For Detection of The Coronavirus In Delta State of Nigeria

Arnold Ojugo; Oghenevwede Debby  Otakore

doi:10.35877/454RI.asci2163

Arnold Ojugo Department of Computer Science, Federal University of Petroleum Resources, Effurun 32001, Delta State, Nigeria (NG)
Oghenevwede Debby Otakore Department of Computer Science, Federal University of Petroleum Resources, Effurun 32001, Delta State, Nigeria (NG)

https://doi.org/10.35877/454RI.asci2163

Keywords: coronavirus, Nigeria, machine learning, malware, Bayesian Network, epidemiology, pandemic, COVID-19

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Abstract

Machine learning algorithm have become veritable tools for effective decision support towards the construction of systems that assist experts (individuals) in their field of exploits and endeavor with regards to problematic tasks.. They are best suited for tasks where data is explored and exploited; and cases where the dataset contains noise, partial truth, ambiguities and in cases where there is shortage of datasets. For this study, we employ the Bayesian network to construct a model trained towards a target system that can help predict best parameters used for classification of the novel coronavirus (covid-19). Data was collected from Federal Medical Center Epidemiology laboratory (a centralized databank for all cases of the covid-19 in Delta State). Data was split into training and investigation (test) dataset for the target system. Results show high predictive capability.

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