Interpretable associations over DataCubes: application to hospital managerial decision making

The world concern about the costs of the health care systems has raised the importance of counting on precise and interpretable tools, that help the health care institution's managers to make decisions to optimize the use of health resources. In this paper we propose a new Classification based on Association Rules (CAR) algorithm that improves the interpretability of the results, making it specially useful for decision making. Changing the usual way to obtain the rules we follow four goals: first to improve the interpretability of the result by obtaining rules meaningful and interpretable by themselves, secondly to reduce the complexity of the result obtaining a lower number of rules; thirdly, to obtain simpler rules, with less size in number of antecedents; and finally to avoid the usual over-fitting problem of the classification methods by obtaining a generic final result set, where specific rules for specific cases are avoided unless they are necessary. To prove the utility of our proposal we have used it in an example of decision support regarding the planning of the surgery rooms.