Covid-19 Patient Mortality Risk Classification Using Bayesian Binary Logistic Regression

Authors

  • Muhammad Qolbi Shobri Universitas Andalas
  • Ferra Yanuar
  • Dodi Devianto

DOI:

https://doi.org/10.20956/j.v18i1.14268

Keywords:

Logistic Regression, Bayesian, Covid-19

Abstract

At the end of 2019 the world was shocked by a new disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). The disease is called Covid-19 (Coronavirus Disease). The mortality rate due to disease is increasing every day. In Indonesia as of April 2021, confirmed Covid-19 patients who died reached 42,530 patients, seeing the high mortality rate of Covid-19 patients so it needs to be studied further so that the risk of death of these Covid-19 patients can be minimized. This research utilizing  binary logistic regression with Bayesian method parameter estimation. In this study, the predictor variables used were in the form of categories that each category in the predictor variables was assumed to have the same risk of death risk of Covid-19 patients. The results of this study indicate that the number of comorbids has a significant effect on the risk of death of Covid-19 patients, the more the number of comorbids suffered by the patient, the higher the risk of death of the patient. The accuracy of this method in classifying data is 84.68%.

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Published

2021-09-02

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Section

Research Articles