Research Article
Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches
Algorithm 1
ML techniques for patient’s symptoms checker.
| Input: Historical Symptoms Data | | Output: Infection probability | (1) | BEGIN | (2) | Initial step: Data collection (save any input patient`s data in the dataset | (3) | Step 1: apply SVM technique to the input data. | (4) | Fit(x,y): curve fitting | (5) | Compute accuracy score | (6) | Compute accuracy precision | (7) | Compute accuracy recall | (8) | Compute accuracy F1-score | (9) | Compute accuracy support | (10) | Step 2: apply KNN technique to the input data. | (11) | Fit(x,y): curve fitting | (12) | Compute accuracy score | (13) | Compute accuracy precision | (14) | Compute accuracy recall | (15) | Compute accuracy F1-score | (16) | Compute accuracy support | (17) | Step 3: apply Logistic function to the input data. | (18) | | (19) | Learning (curve fitting): find optimal parameters that minimize the objective function | (20) | , where | (21) | Predict the infection probability | (22) | Fit(x,y): curve fitting | (23) | Compute accuracy score | (24) | Compute accuracy precision | (25) | Compute accuracy recall | (26) | Compute accuracy F1-score | (27) | Compute accuracy support | (28) | End |
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