A Multinomial Logistic Regression Approach for Arrhythmia Detection

Omar Behadada, Marcello Trovati, Georgios Kontonatsios and Yannis Korkontzelos
Additional contact information
Omar Behadada: University of Tlemcen, Tlemcen, Algeria
Marcello Trovati: Edge Hill Universtiy, Ormskirk, United Kingdom
Georgios Kontonatsios: Edge Hill Universtiy, Ormskirk, United Kingdom
Yannis Korkontzelos: Edge Hill Universtiy, Ormskirk, United Kingdom

International Journal of Distributed Systems and Technologies (IJDST), 2017, vol. 8, issue 4, 17-33

Abstract: Cardiovascular diseases are the leading causes on mortality in the world. Consequently, tools and methods providing useful and applicable insights into their assessment play a crucial role in the prediction and managements of specific heart conditions. In this article, we introduce a method based on multi-class Logistic Regression as a classifier to provide a powerful and accurate insight into cardiac arrhythmia, which is one of the predictors of serious vascular diseases. As suggested by our evaluation, this provides a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilisation of big data in the medical sector.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJDST.2017100102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdst00:v:8:y:2017:i:4:p:17-33

Access Statistics for this article

International Journal of Distributed Systems and Technologies (IJDST) is currently edited by Nik Bessis

More articles in International Journal of Distributed Systems and Technologies (IJDST) from IGI Global
Bibliographic data for series maintained by Journal Editor ().