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

Austrian Journal of Statistics, vol.53, pp.1-10, 2024 (ESCI)

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 Covid-

19.

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.