Abstract
Aims: Laboratory findings in COVID-19 patients vary according to the
severity of the disease. This study aimed at defining a system of
formulas that may predict the presence of thoracic CT involvement, the
extent of such involvement and the need for intensive care stay on the
basis of patient laboratory data using the Waikato Environment for
Knowledge Analysis (WEKA) software. Methods: This study was conducted
with 508 patients whose SARS-CoV-2 RT-PCR test was positive. These
patients were divided into 2 groups, with and without thoracic CT
involvement typical for COVID-19. Then, those patients who had signs of
typical involvement for COVID-19 in their thoracic CT were divided into
3 groups depending on the extent of their lesions. J48 Decision Tree
classification and Linear Regression methods were used on the WEKA
software. The codes implemented in the Python programming language were
used at the estimation, classification and testing stages. Results:
Thoracic CT scans showed that lung involvement was absent in 93 of the
patients, mild in 114, moderate in 115, and severe in 159. The success
rates of WEKA Linear Regression Formulas calculated using laboratory
values and demographic data, respectively 78.92%, 71.69% and 91%. The
success rate of the J48 Decision Tree formula used to predict the
presence of involvement in thoracic CT was found to be 95.95%. The
success rate of the J48 Decision Tree, which was used to predict the
degree of involvement in thoracic CT, was 84.39%. The success rate of
the J48 Decision Tree used to predict the need for intensive care was
found to be 93.06%. Conclusion: 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 and can be used effectively to ensure triage.