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

Omar Behadada, Marcello Trovati, Chich Ma, Nik Bessis

Research output: Contribution to journalArticle

9 Citations (Scopus)
74 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)360-373
Number of pages14
JournalConcurrency and Computation: Practice and Experience
Volume28
Issue number2
Early online date21 Jan 2015
DOIs
Publication statusPublished - 28 Jan 2016

Keywords

  • big data
  • cardiac arrhythmia
  • data analytics
  • fuzzy logic
  • knowledge discovery
  • text mining

Fingerprint Dive into the research topics of 'Big Data Based Extraction of Fuzzy Partition Rules for Heart Arrhythmia Detection: a Semi-Automated Approach'. Together they form a unique fingerprint.

  • Cite this