The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. What is worse, this number continues to increase. Early diagnosis of COVID-19 and finding high-risk patients with a worse prognosis for early prevention is vital. It is essential to screen as many as suspect cases for appropriate quarantine and treatment measures to control the spread of the disease. The viral test based on samples taken from the lower respiratory tract is the critical standard of diagnosis. However, the availability and quality of laboratory tests in the infected area may cause inaccurate results, false positive the combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system (SHC).). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.