Atrial fibrillation (AF) and flutter are common following cardiac surgery, increasing costs and morbidity. Cardiologists need a method to discern those patients who are at high risk for this arrhythmia in order to attempt to treat them by either pharmacologic or non-pharmacologic means. We performed a retrospective analysis of 377 CABG patients, of which 94 developed AF post-operatively. Feature selection and AF occurrence prediction was performed using a multivariate regression model, and two rough set derived rule classifiers. The rough set derived feature subset performed best with an accuracy of 87%, a sensitivity of 58.5%, and a specificity of 96.5%. This shows the importance of testing feature subsets, thereby discouraging the practice of simply combining the best individual predictors. The utility of rough set theory in prediction of cardiac arrhythmia is also validated.