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Association Rules Mining (ARM) is one of the most important tasks of Data mining. The purpose of ARM is to discover relationships having an interest between attributes/patterns stored in very large databases. Nowadays several efficient algorithms have been developed by the researchers for the discovery of relevant Association Rules (ARs). These latter are responsible for decision making in several domains, such as medicine, finance, marketing and many other fields. In this paper, we propose a new algorithm based on exhaustive search to find relevant AR to make the decision and to predict the chance of occurring the Diabetes Mellitus (DM). We develop an algorithm to mine data in less time and less complexity without losing information. Finally, we test our approach using a real database to evaluate the efficiency of our algorithm compared to Apriori algorithm.