Big Data Based Extraction of Fuzzy Partition Rules for Heart Arrhythmia Detection: a Semi-Automated Approach

Behadada, Omar, Trovati, Marcello, Ma, Chich and Bessis, Nik (2016) Big Data Based Extraction of Fuzzy Partition Rules for Heart Arrhythmia Detection: a Semi-Automated Approach. Concurrency and Computation: Practice and Experience, 28 (2). pp. 360-373. ISSN 1532-0626 DOI https://doi.org/10.1002/cpe.3428

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Abstract

In this paper, we introduce a novel method to define semi-automatically fuzzy partition rules to provide a powerful and accurate insight into cardiac arrhythmia. In particular, we define a text mining approach applied to a large dataset consisting of the freely available scientific papers provided by PubMed. The information extracted is then integrated with expert knowledge, as well as experimental data, to provide a robust, scalable and accurate system, which can successfully address the challenges posed by the management and assessment of big data in the medical sector. The evaluation we carried out shows an accuracy rate of 93% and interopretability of 0.646, which clearly shows that our method provides an excellent balance between accuracy and system transparency. Furthermore, this contributes substantially to the knowledge discovery and offers a powerful tool to facilitate the decision-making process.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computing and Information Systems
Date Deposited: 21 Apr 2016 15:23
URI: http://repository.edgehill.ac.uk/id/eprint/7579

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