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This paper presents a novel type 2 diabetes mellitus (T2DM) monitoring system. Decisions on the statuses of diabetes control and predictions of future blood glucose of an individual made by the system depend both on the original medical data collected by medical sensors and some contexts either are entered manually or generated automatically. When dealing with the data, first we clean and transform data and contexts, then build data mining models using several mining algorithms. After the mining process, we analyze and assess the accuracy and sensitivity of the models, and find out the appropriate models for decision making and predicting in diabetes control. Our study is of certain significance to help prevent and treat T2DM.