Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants
Joint Authors
Anwar, Syed Muhammad
Majid, Muhammad
Mustaqeem, Anam
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-05
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Mustaqeem, Anam& Anwar, Syed Muhammad& Majid, Muhammad. 2018. Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1132128
Modern Language Association (MLA)
Mustaqeem, Anam…[et al.]. Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1132128
American Medical Association (AMA)
Mustaqeem, Anam& Anwar, Syed Muhammad& Majid, Muhammad. Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1132128
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132128