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In this article, we proposed an intelligent clinical decision support system for the detection and classification of brain tumor from risk of malignancy index (RMI) images. To overcome the lack of labeled training data needed to train convolutional neural networks, we have used a deep transfer learning and stacking concepts. For this, we choosed seven convolutional neural networks (CNN) architectures already pre-trained on an ImageNet dataset that we precisely fit on magnetic resonance imaging (MRI) of brain tumors collected from the brain tumor segmentation (BraTS) 19 database. To improve the accuracy of our global model, we only predict as output the prediction that obtained the maximum score among the predictions of the seven pre-trained CNNs. We used a 10-way cross-validation approach to assess the performance of our main 2-class model: low-grade glioma (LGG) and high-grade glioma (HGG) brain tumors. A comparison of the results of our proposed model with those published in the literature, shows that our proposed model is more efficient than those published with an average test precision of 98.67%, an average f1 score of 98.62%, a test precision average of 98.06% and an average test sensitivity of 98.33%.

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heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.