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

  • Vinod J. KadamEmail author
  • Samir S. Yadav
  • Shivajirao M. Jadhav
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


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.


Soft-Margin SVM Feature Selection Elitist Genetic Algorithm Arrhythmia classification ECG 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Vinod J. Kadam
    • 1
    Email author
  • Samir S. Yadav
    • 1
  • Shivajirao M. Jadhav
    • 1
  1. 1.Department of Information TechnologyDr. Babasaheb Ambedkar Technological UniversityLonereIndia

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