Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients

Supervised machine learning classificitaion algorithms have been widely used in many fields in recent years. Especially, health is one of the most important areas where machine learning studies are carried out successfully. The aim of this study is to develop models that predict the disease stage of people who apply to hospital with the diagnosis of Covid19. Inadequacies such as intensive care occupancy, insufficiency of beds, and shortage of respiratory equipment are among these problems, and this has left healthcare workers faced with the overwhelming burden of patients. Therefore, estimating the disease stages of Covid-19 patients at an early stage is of great importance. The data set used in the study includes the clinical and laboratory data of the patients during in their admission to the hospital. It has been tried to develop models that predict disease stage by using Logistic Regression, Random Forest and Support Vector Machine algorithms in the data set. The random forest model with 9 variables was the best performing model. With the models obtained, it will be ensured that the hospital management receives information in order to see the necessary treatment for low-risk or high-risk patients and to avoid medical system inadequacies.