This project applies association rules mining and classification based on association rules (CBA) to a synthetic COVID-19 dataset with the goal of supporting patient triage. After cleaning the data (notably dropping body temperature due to synthetic generation flaws), variables are discretized both manually and automatically (MDLP), and apriori rules are mined separately for positive and negative diagnoses. Key findings include that contact with a COVID-19 patient, dry cough, and fever together nearly guarantee a positive result, that age and comorbidities don't predict infection in this dataset, and that gender influences which symptoms are most predictive. The CBA classifier achieves ~96% accuracy on the full dataset and somewhat lower but still strong performance on held-out test data, with perfect specificity on training data.