Educational institutions contain a vast collection of data accumulated for years, so it is difficult to use this data to solve problems related to the progress of the educational process and also contribute to achieving quality. For this reason, the use of data mining techniques helps to extract hidden knowledge that helps in making the decisions necessary to develop education and achieve quality requirements. The data of this study obtained from the College of Business and Economics at Qassim University. Three of the classifiers were compared in this study Decision Tree, Random Forest and Naïve Bayes. The results showed that Random Forest outperforms other algorithms with 71.5% of Precision, 71.2% F1-score, and also it got 71.3% of Recall and Classification Accuracy (CA). This study helps reduce failure by providing an academic advisor to students who have weaknesses in achieving a high-Grade Point Average (GPA). It also helps in developing the educational process by discovering and overcoming weaknesses.

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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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