Logistic Regression Multinomial for Arrhythmia Detection

Omar Behadada; Marcello Trovati; MA Chikh; Nik Bessis; Yannis Korkontzelos

doi:10.1109/FAS-W.2016.39

Logistic Regression Multinomial for Arrhythmia Detection

Omar Behadada, Marcello Trovati, MA Chikh, Nik Bessis, Yannis Korkontzelos

Computer Science

Research output: Contribution to conference › Paper › peer-review

6 Citations (Scopus)

200 Downloads (Pure)

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.

Original language	English
Pages	133-137
DOIs	https://doi.org/10.1109/FAS-W.2016.39
Publication status	E-pub ahead of print - 19 Dec 2016
Event	2nd International Workshop on Data-driven Self-regulating Systems - Augsburg, Germany Duration: 12 Sept 2016 → …

Workshop

Workshop	2nd International Workshop on Data-driven Self-regulating Systems
Country/Territory	Germany
City	Augsburg
Period	12/09/16 → …

Keywords

Multinomial Logistic Regression
Knowledge Extraction
Big Data

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/FAS-W.2016.39

DSS_paper_post_review.pdfAccepted author manuscript, 346 KBLicence: CC BY-NC-ND

http://ieeexplore.ieee.org/document/7789455/

Cite this

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title = "Logistic Regression Multinomial for Arrhythmia Detection",

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.",

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author = "Omar Behadada and Marcello Trovati and MA Chikh and Nik Bessis and Yannis Korkontzelos",

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T1 - Logistic Regression Multinomial for Arrhythmia Detection

AU - Behadada, Omar

AU - Trovati, Marcello

AU - Chikh, MA

AU - Bessis, Nik

AU - Korkontzelos, Yannis

PY - 2016/12/19

Y1 - 2016/12/19

N2 - 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.

AB - 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.

KW - Multinomial Logistic Regression

KW - Knowledge Extraction

KW - Big Data

UR - http://dss2016.inn.ac/#program

U2 - 10.1109/FAS-W.2016.39

DO - 10.1109/FAS-W.2016.39

M3 - Paper

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EP - 137

T2 - 2nd International Workshop on Data-driven Self-regulating Systems

Y2 - 12 September 2016

ER -

Logistic Regression Multinomial for Arrhythmia Detection

Abstract

Workshop

Keywords

UN SDGs

Access to Document

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