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Clinical medicine is facing a challenge of knowledge discovery from the growing volume of data. In this paper, a data mining algorithm (the G-algorithm) is proposed for extraction of robust rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. The G-algorithm is applied to a data set obtained for children born with a malformation of the heart (univentricular heart). As a result of the Fontan surgical procedure, designed to palliate the children, 10%-35% of patients post-operatively develop an arrhythmia known as intra-atrial re-entrant tachycardia. There is an obvious need to identify those children that may develop the tachycardia before the surgery is performed. Prior attempts to identify such children with statistical techniques have been unrewarding. The G-algorithm shows that there exists an unambiguous relationship between measurable features and the tachycardia. The data set used in this study shows that, for 78.08% of infants, the occurrence of tachycardia can be accurately predicted. The authors' prior computational experience with diverse medical data sets indicates that the percentage of accurate predictions may become even higher if data on additional features is collected for a larger data set.