Classification of Cardiac Arrhythmia using improved Feature Selection methods and Ensemble Classifiers

Rajat Jain, Pranam R. Betrabet, B. Ashwath Rao, N. V.Subba Reddy

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Arrhythmia is one of the life-threatening heart diseases which is diagnosed and analyzed using electrocardiogram (ECG) recordings and other symptoms namely rapid heartbeat or chest-pounding, shortness of breath, near fainting spells, insufficient pumping of blood from the heart, etc along with sudden cardiac arrest. Arrhythmia records a hasty and aberrant ECG. In this implementation, the arrhythmia dataset is collected from the UCI machine learning repository and then classified the records into sixteen stated classes using multiclass classification. The large feature set of the dataset is reduced using improved feature selection techniques such as t-Distributed Stochastic Neighbor Embedding (TSNE), Principal Component Analysis (PCA), Uniform Manifold Approximation, and Projection (UMAP) and then an Ensemble Classifier is built to analyse the classification accuracy on arrhythmia dataset to conclude when and which approach gives optimal results.

Original languageEnglish
Article number012003
JournalJournal of Physics: Conference Series
Volume2161
Issue number1
DOIs
Publication statusPublished - 11-01-2022
Event1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 - Manipal, Virtual, India
Duration: 28-10-202130-10-2021

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy

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