Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ2 statistical model is used to rank the commonly used 13 HF features. Based on the χ2 test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ2-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ2-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%.

Concept maps are playing an increasingly important role in various computing fields. In particular, they have been popularly used for organizing and representing knowledge. However, constructing concept maps manually is a complex and time-consuming task. Therefore, the creation of concept maps automatically or semi-automatically from text documents is a worthwhile research challenge. Recently, various approaches for automatic or semi-automatic construction of concept maps have been proposed. However, these approaches suffer from several limitations. First, only the noun phrases in text documents are included without resolution of the anaphora problems for pronouns. This omission causes important propositions available in the text documents to be missed, resulting in decreased recall. Second, although some approaches label the relationship to form propositions, they do not show the direction of the relationship between the subject and object in the form of Subject–Relationship–Object, leading to ambiguous propositions. In this paper, we present a cluster-based approach to semi-automatically construct concept maps from text documents. First, we extract the candidate terms from documents using typed dependency linguistic rules. Anaphoric resolution for pronouns is introduced to map the pronouns with candidate terms. Second, the similarities are calculated between the pairs of extracted candidate terms of a document and clusters are made through affinity propagation by providing the calculated similarities between the candidate terms. Finally, the extracted relationships are assigned between the candidate terms in each cluster. Our empirical results show that the semi-automatically constructed concept maps conform to the outputs generated manually by domain experts, since the degree of difference between them is proportionally small based on a Likert scale. Furthermore, domain experts verified that the constructed concept maps are in accordance with their knowledge of the information system domain.