A Semantic approach for extracting medical association rules

Abstract

Healthcare sector has large amounts of data that require careful analysis in order to improve the medical service offered to the patients. Semantic data mining can play an effective rule in analyzing such amounts of data. In this paper, we propose a framework for association rule extraction based on ontology semantics. In the proposed framework, traditional medical datasets are represented using web ontology language. The medical dataset is transformed into an ontology of the form of triples (subject, object, predicate), and SPARQL is used to query the generated ontology. The Apriori algorithm is used to generate the association rules. Intensive experiments have been conducted to measure the quality and significance of the resulting association rules under different scenarios using different support and confidence values. The obtained results have shown that ontology-based Apriori algorithm is much better than the traditional Apriori algorithm. The rules generated using both algorithms have been compared in terms of several performance metrics including the number of frequent items, the number of generated rules, the computation time, the memory consumption, and the average confidence of the generated rules. The different performance metrics revealed the superiority of the proposed semantic Apriori algorithm (the ontology-based Apriori) compared to traditional Apriori algorithm.