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This paper analyzes data from coronary artery bypass grafting (CABG) using decision functions to represent rules. The data was collected at the University Hospital in Umeå, Sweden. The data contains pre-, intra-, and postoperative detail from 2975 heart operations during 1993-96. Each instance is represented by 14 preoperative variables, 4 intraoperative variables, and 9 postoperative variables. A logical method is used to predict the postoperative variables using preoperative variables. First, each postoperative variable is represented as a decision functions of preoperative variables. Then, for each postoperative variable, a minimal set of preoperative variables is derived. And finally, each postoperative variable is represented by a minimum set of rules using preoperative variables. With this method we can predict postoperative outcome, where prediction using preoperative data only is of particular interest e.g. for surgery scheduling.