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Nowadays, there is a significant increase in the medical data that we should take advantage of it. The application of the machine learning via the data mining processes, such as data classification, depends on using a single classification algorithm or those combined such as ensemble models. The objective of this work is to improve the classification accuracy of previous results for Alzheimer disease diagnosing. The Decision Tree algorithm was combined with three types of ensemble methods, which are Boosting, Bagging and Stacking. The clinical dataset from the Open Access Series of Imaging Studies (OASIS) was used in the experiments. The experimental results of the proposed approach were better than the previous work results. Where the Random Forest (Bagging) achieved the highest accuracy among all algorithms with 96.66%, while the lowest result was Decision Tree with 73.33%, all these results generated in this paper are higher in accuracy than that done before.

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