Machine learning with bayesian networks for Covid-19 data

The Covid-19 pandemic emerged on November 17, 2019, in Wuhan Province of China. The outbreak was initially detected in those found in the seafood and animal market in this region. Later, it spread from person to person and spread to other cities in Hubei province, especially in Wuhan, other provinces of China, and other world countries. Until May 11, 2022, 519.682.000 cases occurred globally and 6.266.278 patients died from Covid-19. Many research and analysis studies have been conducted in our country and around the world showing the effects of the Covid-19 pandemic. In this thesis study, 215,968 worldwide cases from 104 countries around the world were analyzed and the patients were tried to be classified using Bayesian Networks and machine learning techniques. In this study, it was investigated whether the patients who caught the Covid-19 virus would survive using nine variables. In this way, it will be determined which patient should be given priority and treated or kept under observation. Thus, this study aims to reduce the death rates due to the Covid-19 pandemic worldwide.