Özet:

The aim of the study is to calculate the mortality risk of patients diagnosed with Covid-19 using Machine Learning algorithms. In the study, demographic and clinical data of patients admitted to the health facility with the diagnosis of Covid-19 in Atlanta, Georgia, which are published as open access on the web, are used. The mortality risk of the patients is calculated using Machine Learning algorithms called Decision Tree, Random Forest and Adaptive Boost based on the data. It is observed that the demographic and clinical findings of the patients are effective on mortality risks and that the Machine Learning-based prediction modeling created in this direction can be applied with high reliability (Acc=83.5). With the findings obtained, high-reliability classification models can be created using Machine Learning methods and decision support modules can be created that can guide clinicians and health professionals in patient prioritization in line with the calculation of mortality risks of patients. By creating web-based modules, a scientific basis is established for health authorities, clinicians and hospital managers to make effective and efficient preparations for bed occupancy planning. Unnecessary health expenditures and patients who are likely to have a relatively mild illness can be prevented from receiving unnecessary treatment.

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