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Big data‐based extraction of fuzzy partition rules for heart arrhythmia detection: a semi‐automated approach
Author(s) -
Behadada Omar,
Trovati Marcello,
Chikh MA,
Bessis Nik
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3428
Subject(s) - interpretability , computer science , data mining , scalability , partition (number theory) , fuzzy logic , big data , knowledge extraction , artificial intelligence , database , mathematics , combinatorics
Summary 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 interpretability 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. Copyright © 2015 John Wiley & Sons, Ltd.