Classification of The Risk of Comorbid Covid-19 Patient at Bengkalis Hospital Using Bayesian Binary Logistics Regression
DOI:
https://doi.org/10.37385/jaets.v3i2.812Keywords:
Binary Logistic Regression, COVID-19, ComorbidAbstract
COVID-19 is an infectious disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). This viral infection causes illness with symptoms ranging from mild to severe. The number of deaths from this disease is increasing day by day. A person who is most easily infected with the COVID-19 virus is a person who has a comorbid disease, because the body's immunity decreases due to the impact of a previous illness. The purpose of this study was to determine the comorbid factors that trigger a person's death due to COVID-19. This research uses binary logistic regression with Bayes method parameter estimation. In this study, the predictor variables used were in the form of categories. The results showed that the factors that influence the death of a person on the death of COVID-19 in comorbid diseases are Diabetes Mellitus and Pneumonia.
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