DISCUSSION
A literature search showed that there were no clinical studies on this subject. Our study is the first one conducted on this subject. The closest to our study was the one made by Alakuş et al.11 where they developed clinical models predicting COVID-19 using a hypothetical deep learning model based on the laboratory data of COVID-19 patients. They tested 18 laboratory findings of 600 patients and found that their experimental estimation model detected the presence of COVID-19 with 86.6% accuracy, 86.7% specificity and 62.5% AUC.
Using the WEKA Linear Regression Formula, the formulas found in our study estimated the presence of involvement on thoracic CT at a rate of 78.92%, the extent of involvement on thoracic CT at 71.69%, and the need for intensive care in patients with thoracic CT involvement at 91%. Using the J48 Decision Tree Formula, the formulas found in the study estimated the presence of involvement on thoracic CT at a rate of 95.95%, the extent of involvement on thoracic CT at 84.39%, and the need for intensive care at 93.06%. A major limitation of this study is that the comorbidities of patients could not be assessed.
In the COVID-19 pandemic regions, it is important to diagnose the disease quickly and obtain medical resources. Limited medical resources have become a huge problem in pandemic regions. It is also quite important to determine the severity of the disease and identify priorities in treatment. The WEKA Linear Regression Formulas and J48 Decision Trees that were constructed with the help of the deep learning method in our study were able to estimate the presence of lung involvement, the extent of such involvement and the need for intensive care in COVID-19 patients at a high rate. We think that the results of this study will facilitate revealing the presence of lung involvement and identification of critical patients in the COVID-19 pandemic and particularly under circumstances where cases exponentially increase and resources are restricted, and can be used effectively to ensure triage.