Welcome to the InfoSci Platform
Reference Hub4
A Multinomial Logistic Regression Approach for Arrhythmia Detection

A Multinomial Logistic Regression Approach for Arrhythmia Detection

Omar Behadada, Marcello Trovati, Georgios Kontonatsios, Yannis Korkontzelos
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 17
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781522514046|DOI: 10.4018/IJDST.2017100102
Cite Article Cite Article

MLA

Behadada, Omar, et al. "A Multinomial Logistic Regression Approach for Arrhythmia Detection." IJDST vol.8, no.4 2017: pp.17-33. http://doi.org/10.4018/IJDST.2017100102

APA

Behadada, O., Trovati, M., Kontonatsios, G., & Korkontzelos, Y. (2017). A Multinomial Logistic Regression Approach for Arrhythmia Detection. International Journal of Distributed Systems and Technologies (IJDST), 8(4), 17-33. http://doi.org/10.4018/IJDST.2017100102

Chicago

Behadada, Omar, et al. "A Multinomial Logistic Regression Approach for Arrhythmia Detection," International Journal of Distributed Systems and Technologies (IJDST) 8, no.4: 17-33. http://doi.org/10.4018/IJDST.2017100102

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.