Association rule mining provides valuable information in terms of significant correlations between different attributes’ values that might not be evident at the first glance in large datasets. The experimental part of this work has demonstrated benefits of integration of interactivity in Apriori approach for discovering association rules hidden in the target dataset. The interactive algorithm for discovering association rules starts by asking user’s requirement with respect to attributes to be included in the search. Since the dataset has one class attribute that determines the patient class (LIVE or DIE), the clinicians are interested in finding rules that determine the value of patient class (LIVE or DIE). In addition to attribute specification, the user supplies the minimum support and confidence threshold, the two parameters required by Apriori algorithm. In the experimental runs, minimum support and confidence threshold have been fixed at 15% and 80%, respectively

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