Fatality Prediction of COVID-19 by using Machine Learning: Oman Dataset

COVID-19 is a new type of coronavirus that cause a range of symptoms in human, such as fever, breathing difficulties, tiredness, dry cough, and severe acute respiratory syndrome. In more serious cases, COVID-19 could lead to death. This paper presents the outcomes of a cohort study of 467 confirmed COVID-19 patients in Oman. Machine Learning-algorithms were employed to extract the hidden patterns and identify the factors 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. The findings also suggested that age, gender, chronic kidney disease, and other complete blood count parameters are risk factors for poor prognosis in older patients. The obtained results are promising as they give insight into the main causes of patient status: recovery and death.