A Multinomial Logistic Regression Approach for Arrhythmia Detection

Omar Behadada, Marcello Trovati, Georgios Kontonatsios, Yannis Korkontzelos

Research output: Contribution to journalArticle (journal)peer-review


Cardiovascular diseases are the leading causes on mortality in the world. As a consequence, 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.
Original languageEnglish
Pages (from-to)17-33
JournalInternational Journal of Distributed Systems and Technologies (IJDST)
Issue number4
Early online date31 Dec 2018
Publication statusE-pub ahead of print - 31 Dec 2018


Dive into the research topics of 'A Multinomial Logistic Regression Approach for Arrhythmia Detection'. Together they form a unique fingerprint.

Cite this