The healthcare ambit is usually perceived as “information rich” yet “knowledge poor”. Nowadays, an unprecedented effort is underway to increase the use of business intelligence techniques to solve this problem. Heart disease (HD) is a major cause of mortality in modern society. This paper analyzes the risk factors that have been identified in cardiovascular disease (CVD) surveillance systems. The Heart Care study identifies attributes related to CVD risk (gender, age, smoking habit, etc.) and other dependent variables that include a specific form of CVD (diabetes, hypertension, cardiac disease, etc.). In this paper, we combine Clustering, Association Rules, and Neural Networks for the assessment of heart-event-related risk factors, targeting the reduction of CVD risk. With the use of the K-means algorithm, significant groups of patients are found. Then, the Apriori algorithm is applied in order to understand the kinds of relations between the attributes within the dataset, first looking within the whole dataset and then refining the results through the subsets defined by the clusters. Finally, both results allow us to better define patients’ characteristics in order to make predictions about CVD risk with a Multilayer Perceptron Neural Network. The results obtained with the hybrid information mining approach indicate that it is an effective strategy for knowledge discovery concerning chronic diseases, particularly for CVD risk.

The use of e-learning in education is an ever-increasing practice. E-learning could generate effective learning for education. There are several factors affecting the creation of successful e-learning for education as well as several criteria possibly applied to evaluate the effectiveness. The “traditional” way (questionnaire, interview, information system analysis) to measure effectiveness is not enough in e-learning measure of effectiveness because part of the information, that coming from social networks, will be lost. This paper, after identifying the Critical Success Factors (CSFs) of a synchronous e-learning system, and identifying the Key Performance Indicators (KPIs), proposes an approach for evaluation based on the analysis of information derived from social aspects. The paper proposes a set of CSFs and KPIs to study the students’ perception of e-learning platform and highlights how to measure the KPIs using social software information.

E-learning assessment is a key aspect in the overall e-learning process. There are several parameters to consider during the assessment. In recent years, several sets of factors, called Critical Success Factors, have been defined to provide a structural approach to assessment. They focus on many aspects but, in our view, they do not properly consider student satisfaction with courses. In e-learning applications, student opinion must be examined where it is expressed: on e learning course social pages and/or social pages outside the platform but specific to the e-learning course. The problem is that these resources are unstructured and thus it is important to structure these resources before using them for assessment. In this paper, we discuss a proposal that can capture student opinion from social pages, combining several techniques, such as Natural Language Processing, Information Extraction; ontologies that help us to understand what and how students discuss about e-learning courses.