Cardiac Arrhythmia Detection Using Ensemble of Machine Learning Algorithms

  • R. Nandhini AbiramiEmail author
  • P. M. Durai Raj Vincent
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)


An ECG signal is a bioelectrical signal which records the electrical activity of the heart. ECG signals are used as the parameter for detecting various heart diseases. Cardiac arrhythmia can be detected using ECG signals. Arrhythmia is a condition in which the rhythm of the heart is irregular, too slow or too fast. The data for this work is obtained from the University of California, Irvine machine learning repository. The data obtained from the repository is preprocessed. Feature selection is made, and machine learning models are applied to the preprocessed data. Finally, data is classified into two classes, namely normal and arrhythmia. Feature selections were made to optimize the performance of machine learning algorithms. Features with more number of missing values and which showed no variation for all the instances have been deleted. Accuracy achieved using ensemble of machine learning algorithms is 85%. The objective of this research is to design a robust machine learning algorithm to predict cardiac arrhythmia. The prediction of cardiac arrhythmia is performed using ensemble of machine learning algorithms. This is to boost the accuracy achieved by individual machine learning algorithms. The technique of combining two or more machine learning models to improve the accuracy of the results is called ensemble prediction. More accurate results can be achieved using ensemble methods than the results achieved using single machine learning model.


Electrocardiogram Machine learning Cardiac arrhythmia and classification 


  1. 1.
    Kelwade, J.P., Salankar, S.S.: Prediction of cardiac arrhythmia using artificial neural network. Int. J. Comput. Appl. 115(20) (2015)Google Scholar
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
    Rai, H.M., Trivedi, A.: Classification of ECG waveforms for abnormalities detection using DWT and back propagation algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 1(4), 517 (2012)Google Scholar
  7. 7.
    Sarkaleh, M.K., Shahbahrami, A.: Classification of ECG arrhythmias using discrete wavelet transform and neural networks. Int. J. Comput. Sci. Eng. Appl. 2(1), 1 (2012)Google Scholar
  8. 8.
    Jaiswal, G.K., Paul, R.: Artificial neural network for ECG classification. Recent Res. Sci. Technol. 6(1) (2014)Google Scholar
  9. 9.
    Sadr, A., Mohsenifar, N., Okhovat, R.S.: Comparison of MLP and RBF neural networks for prediction of ECG signals. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 11(11), 124–128 (2011)Google Scholar
  10. 10.
    Tang, X., Shu, L.: Classification of electrocardiogram signals with RS and quantum neural networks. Int. J. Multimed. Ubiquitous Eng. 9(2), 363–372 (2014)CrossRefGoogle Scholar
  11. 11.
    Belgacem, N., Chikh, M.A., Reguig, F.B.: Supervised Classification of ECG Using Neural Networks (2003)Google Scholar
  12. 12.
    Kshirsagar, P.R., Akojwar, S.G., Dhanoriya, R.: Classification of ECG-Signals Using Artificial Neural NetworksGoogle Scholar
  13. 13.
    Sao, P., Hegadi, R., Karmakar, S.: ECG signal analysis using artificial neural network. Int. J. Sci. Res. National Conference on Knowledge, Innovation in Technology and Engineering, pp. 82–86 (2015)Google Scholar
  14. 14.
    Mitra, M., Samanta, R.K.: Cardiac arrhythmia classification using neural networks with selected features. Procedia Technol. 10, 76–84 (2013)CrossRefGoogle Scholar
  15. 15.
    Gayathri, B.M., Sumathi, C.P.: Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–5. IEEE (2016)Google Scholar
  16. 16.
    Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San Tan, R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)CrossRefGoogle Scholar
  17. 17.
    Yıldırım, Ö., Pławiak, P., Tan, R.S., Acharya, U.R.: Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput. Biol. Med. 102, 411–420 (2018)CrossRefGoogle Scholar
  18. 18.
    Pławiak, P.: Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst. Appl. 92, 334–349 (2018)CrossRefGoogle Scholar
  19. 19.
    Pławiak, P.: Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol. Comput. 39, 192–208 (2018)CrossRefGoogle Scholar
  20. 20.
    Banu, G.R.: Predicting Thyroid Disease Using Linear Discriminant Analysis (LDA) Data Mining TechniqueGoogle Scholar
  21. 21.
    Chaurasia, V.: Early Prediction of Heart Diseases Using Data Mining Techniques (2017)Google Scholar
  22. 22.
    Medhekar, D.S., Bote, M.P., Deshmukh, S.D.: Heart disease prediction system using naive Bayes. Int. J. Enhanced Res. Sci. Technol. Eng. 2(3) (2013)Google Scholar
  23. 23.
    Deekshatulu, B.L., Chandra, P.: Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technol. 10, 85–94 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Vellore Institute of TechnologyVelloreIndia

Personalised recommendations