Abstract
The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. The new coronavirus is the cause of the Covid-19 disease, which kills many people in the world every day. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and hygienic principles are the most well-known strategies to prevent Covid-19 infection. In this research, we have tried to examine the symptoms of Covid-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the SMOTE up-sampling method and then developed some classification models to predict the recovery or death of patients. Besides, we implemented a rule-based technique to identify important symptoms that affect patients' fate and calculate the range of values in these features that lead to recovery or death of patients. Our results showed that the random forest model with 94% accuracy, 95.2% sensitivity, 92.7% specification, 93.2% precision, and 94.2% F-score outperforms state-of-the-art classification models. Finally, we identified the ten most significant rules in the data set. The rules state that different combinations of 6 features in certain ranges of their values lead to patients' recovery with 90% confidence. In conclusion, the classification results in this study show better performance than recent researches. Besides, help physicians consider other important factors in improving health services to different groups of Covid-19 patients.