Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants
type: Item
instance of: scholarly article
title: Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants
zbMATH Open document ID: 1411.92142
DOI: 10.1155/2018/7310496
author: Anam Mustaqeem
author: Syed Muhammad Anwar
author: Muahammad Majid
published in: Computational \& Mathematical Methods in Medicine
publication date: 2019-03-27T00:00:00Z
review text: Summary: Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11\% when used with 80/20 data split and 92.07\% using 90/10 data split option.
Mathematics Subject Classification ID: 92C50
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7043211
zbMATH Keywords: cardiac arrhythmia
zbMATH Keywords: classification
zbMATH Keywords: support vector machine
Wikidata QID: Q55024670
describes a project that uses: Boruta
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/7310496
OpenAlex ID: W2790036160
cites work: Feature selection with dynamic mutual information
cites work: Toward optimal feature selection using ranking methods and classification algorithms
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description: scientific article; zbMATH DE number 7043211