Predicting student academic performance by means of associative classification

The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments

A Pathogenetic Focus on the Aortoiliac - Mesenteric Steal Syndrome

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Mediterranean diet impact on cardiovascular diseases: a narrative review

: Cardiovascular disease (CVD) accounts for more than 17 million deaths per year worldwide. It has been estimated that the influence of lifestyle on CVD mortality amounts to 13.7% for smoking, 13.2% for poor diet, and 12% for inactive lifestyle. These results deeply impact both the healthy status of individuals and their skills in working. The impact of CVD on productivity loss accounts for the 24% in total costs for CVD management.Mediterranean diet (MedD) can positively impact on natural history of CVD. It is characterized by a relatively high consumption of inexpensive and genuine food such as cereals, vegetables, legumes, nuts, fish, fresh fruits, and olive oil as the principal source of fat, low meat consumption and low-to-moderate consumption of milk, dairy products, and wine.Its effects on cardiovascular health are related to the significant improvements in arterial stiffness. Peripheral artery disease, coronary artery disease, and chronic heart failure are all positively influenced by the MedD. Furthermore, MedD lowers the risk of sudden cardiac death due to arrhythmias.The present narrative review aims to analyze the effects of MedD on CVD