Abstract
Epidemic diseases have been seen frequently in recent years. Today’s, thanks to advanced database systems, it is possible to reach the clinical and demographic data of citizens. With the help of these data, machine learning algorithms can predict how severe (at home, hospital or intensive care unit) the disease will be experienced by patients in the risk group before the epidemic begins to spread. With these estimates, necessary precautions can be taken. In this study, during the COVID-19 epidemic, the data obtained from the Italian national drug database was used. COVID-19 severity and the features (Age, Diabetes, Hypertension etc.) that affect the severity was estimated using data mining (CRISP-DM method), machine learning approaches (Bagged Trees, XGBoost, Random Forest, SVM) and an algorithm solving the unbalanced class problem (SMOTE). According to the experimental findings, the Bagged Classification and Regression Trees (Bagged CART) yielded higher accuracy COVID-19 severity prediction results than other methods (83.7%). Age, cardiovascular diseases, hypertension, and diabetes were the four highest significant features based on the relative features calculated from the Bagged CART classifier. The proposed method can be implemented without losing time in different epidemic diseases that may arise in the future.
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