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A Multinomial Logistic Regression Approach for Arrhythmia Detection

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  • 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)

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.

Suggested Citation

  • Omar Behadada & Marcello Trovati & Georgios Kontonatsios & Yannis Korkontzelos, 2017. "A Multinomial Logistic Regression Approach for Arrhythmia Detection," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 8(4), pages 17-33, October.
    • Handle: RePEc:igg:jdst00:v:8:y:2017:i:4:p:17-33
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