Research Article
An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease
Table 1
Features extracted using different feature selection methods.
| Feature selection methods | Israeli COVID-19 dataset | Symptoms and COVID-19 presence dataset |
| Chi-square | 1. Cough 2. Fever 3. Sore throat 4. Headache 5. Test indication | 1. Breathing problem 2. Fever 3. Dry cough 4. Sore throat 5. Abroad travel 6. Contact with COVID-19 patient 7. Attended large gathering |
| RFE | 1. Cough 2. Sore throat 3. Headache 4. Age of 60 years and above 5. Test indication | 1. Breathing problem 2. Fever 3. Dry cough 4. Sore throat 5. Abroad travel 6. Contact with COVID-19 patient 7. Attended large gathering |
| GA | 1. Cough 2. Sore throat 3. Headache | 1. Breathing problem 2. Sore throat 3. Asthma 4. Chronic lung disease 5. Abroad travel 6. Contact with COVID-19 patient 7. Wearing masks |
| PSO | 1. Cough 2. Fever 3. Sore throat 4. Shortness of breath 5. Gender | 1. Breathing problem 2. Fever 3. Dry cough 4. Sore throat 5. Running nose 6. Abroad travel 7. Visited public exposed places |
| Random forest | 1. Cough 2. Fever 3. Sore throat 4. Headache 5. Test indication | 1. Breathing problem 2. Fever 3. Dry cough 4. Sore throat 5. Abroad travel 6. Contact with COVID-19 patient 7. Attended large gathering |
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