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.