SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples

Gök, Elif Ceren; Olgun, Mehmet Onur

An increase in the number of patients and death rates make Covid-19 a serious pandemic situation. This problem has effects on health security, economical security, social life, and many others. The long and unreliable diagnosis process of the Covid-19 makes the disease spread even faster. Therefore, fast and efficient diagnosis is significant for dealing with this pandemic. Computer-aided medical diagnosis systems are very common applications and due to the importance of the problem, providing accurate predictions is required. In this study, blood samples of patients from Einstein Hospital in Brazil has collected and used for prediction on the severity level of Covid-19 with machine learning algorithms. The study was constructed in two stages; in stage-one, no preprocessing method has applied while in stage-two preprocessing has emphasized for achieving better prediction results. At the end of the study, 0.98 accuracy was obtained with the tuned Random Forest algorithm and several preprocessing methods.