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Soft-Margin SVM Incorporating Feature Selection Using Improved Elitist GA for Arrhythmia Classification

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

Cardiac arrhythmia is one of the serious heart disorders. In many cases; it may lead to stroke and heart failure. Therefore timely and accurate diagnosis is very necessary. In this paper, we proposed a novel ECG Arrhythmia classification approach which includes an Elitist-population based Genetic Algorithm to optimally select the important features and the Soft-Margin SVM as a base classifier to diagnose arrhythmia by classifying it into normal and abnormal classes. Our improved GA employs the classification error obtained by 10 fold cross-validated SVM classification model as a fitness value. The aim of the Genetic Algorithm is therefore to minimize this value. To show the effectiveness of the proposed method, the UCI ECG arrhythmia dataset was used. Performance of base classifier soft-margin SVM was analyzed with different values of the penalty parameter C. Proposed feature selection method significantly enhances the accuracy and generates fewer and relevant input features for the classifier. With the introduced model, we obtained a promising classification accuracy value. The result of the study proves that the model is also comparable with the existing methods available in the literature. The simulation results and statistical analyses are also showing that the proposed model is truly beneficial and efficient model for cardiac ECG Arrhythmia classification.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Vinod J. Kadam, Samir S. Yadav & Shivajirao M. Jadhav

Authors
  1. Vinod J. Kadam
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  2. Samir S. Yadav
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  3. Shivajirao M. Jadhav
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Corresponding author

Correspondence to Vinod J. Kadam .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Kadam, V.J., Yadav, S.S., Jadhav, S.M. (2020). Soft-Margin SVM Incorporating Feature Selection Using Improved Elitist GA for Arrhythmia Classification. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_94

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