Logistic Regression Multinomial for Arrhythmia Detection

Behadada, Omar, Trovati, Marcello, Chikh, MA, Bessis, Nik and Korkontzelos, Yannis (2016) Logistic Regression Multinomial for Arrhythmia Detection. The 2nd International Workshop on Data-driven Self-regulating Systems (DSS 2016), 12 September 2016, Augsburg, Germany, pp. 133-137, ISBN 978-1-5090-3651-6, DOI https://doi.org/10.1109/FAS-W.2016.39.

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Abstract

In this paper, we introduce a method based on logistics Regression multi-class as a classifier to provide a powerful and accurate insight into cardiac arrhythmia. As suggested by our evaluation, this provide a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilization of big data in the medical sector.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Multinomial Logistic Regression, Knowledge Extraction, Big Data
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Computing and Information Systems
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Date Deposited: 21 Oct 2016 09:53
URI: http://repository.edgehill.ac.uk/id/eprint/8036

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