A Machine Learning Based Exploration of Covid19 Mortality Risk: Oman Dataset

COVID-19 is a new type of coronavirus that cause a range of diseases to human. Symptoms include fever, breathing difficulties, tiredness, dry cough, and severe acute respiratory syndrome. In more serious cases, Covid19 could lead to death. This paper presents the outcomes in a cohort of 467 confirmed COVID-19 patients in Oman. Machine Learning-algorithms were employed to extract the hidden patterns and identify the causes of death or survival from the obtained datasets. The 10-fold Cross Validation was applied to ensure the reliability of the results. The experimental results demonstrated that some parameters contribute significantly to the death of the infected patients. It has been revealed that, Sodium, Hemoglobin, Mean Cell Volume, Chloride and Eosinophil are the most significant factors in predicting the progression of the disease and the final outcome. It has also been found that age, gender, chronic kidney disease, and other complete blood count parameters are risk factors for poor progression in older patients. The obtained results are promising as they give an insight knowledge about the main causes of patient status: recovery and death.