Prediction of Epidemic Disease Severity and the Relative Importances of the Factors for Epidemic Disease Using the Machine Learning Methods

dc.contributor.authorKutlu, Hüseyin
dc.contributor.authorDogan, Cagla Nur
dc.contributor.authorDoğan, Çağla Nur
dc.contributor.authorTurgut, Mehmet
dc.date.accessioned2024-08-04T19:54:40Z
dc.date.available2024-08-04T19:54:40Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractEpidemic 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.en_US
dc.identifier.doi10.46810/tdfd.1110094
dc.identifier.endpage34en_US
dc.identifier.issn2149-6366
dc.identifier.issue3en_US
dc.identifier.startpage24en_US
dc.identifier.trdizinid1188885en_US
dc.identifier.urihttps://doi.org/10.46810/tdfd.1110094
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1188885
dc.identifier.urihttps://hdl.handle.net/11616/90037
dc.identifier.volume11en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofTürk Doğa ve Fen Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titlePrediction of Epidemic Disease Severity and the Relative Importances of the Factors for Epidemic Disease Using the Machine Learning Methodsen_US
dc.typeArticleen_US

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