Comparison between ANN-Based Heart Stroke Classifiers Using Varied Folds Data Set Cross-Validation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 308)

Abstract

This paper presents the design and development of an artificial neural network (ANN) model for the classification of heart stroke disease. The novelty of this work is training multilayer perceptron (MLP) neural network architectures with back-propagation algorithm for multivariate large data sets. Subsequently, the performances of the ANN models are evaluated using database of heart stroke obtained from Cleveland Clinic Foundation Database with all attributes are numeric-valued. The accuracy of the designed ANN models are cross-validated using varied folds of data set comprising 303 instances extracted from different age groups. This study exhibits ANN-based prognosis for early detection of level of heart stroke with testing classification accuracy of 85.55 %.

Keywords

Artificial neural network Multilayer perceptron Coronary heart disease 

Notes

Acknowledgments

Our thanks to the experts who have contributed in development of Cleveland Heart Disease database.

References

  1. 1.
    McGovern, P.G., Pankow, J.S., Shahar, E., Doliszny, K.M., Folsom, A.R., Blackburn, H., Luepker, R.V.: Recent trends in acute coronary heart disease: mortality, morbidity, medical care, and risk factors. New England J. Med. 334(14), 884–890 (1996)CrossRefGoogle Scholar
  2. 2.
    Mateny, M., McPheeters, M.L, Glasser, A., Mercaldo, N., Weaver, R.B, Jerome, R.N, et al.: Systematic review of cardiovascular disease risk assessment tools. Evidence Synthesis/Technology Assessment No. 85. AHRQ Report No. 11-05155-EF-1. Rockville, Agency for Healthcare Research and Quality (2011)Google Scholar
  3. 3.
    Chen, J., Greiner, R.: Comparing Bayesian network classifiers In: Proceedings of UAI-99, pp. 101–108. Morgan Kaufmann, San Francisco (1999)Google Scholar
  4. 4.
    The Expert Panel: National cholesterol education program second report of the expert panel on detection, evaluation and treatment of high blood cholesterol in adults (adult treatment panel II). Circulation 89(3), 1333–1445 (1994)CrossRefGoogle Scholar
  5. 5.
    Niranjana Murthy, H.S., Meenakshi, M.: ANN model to predict coronary heart disease based on risk factors. Bonfring Int. J. Man Mach Interface 3(2), 13–18 (2013)CrossRefGoogle Scholar
  6. 6.
    Neuro Intelligence using Alyuda: Source available at www.alyuda.com. Last accessed 10 Jan 2014
  7. 7.
    Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)CrossRefGoogle Scholar
  8. 8.
    Mehmood, A.M., Kuppa, M.R.: A novel pruning approach using expert knowledge for data-specific pruning. Eng. Comput. 28(1), 21–30 (2012)CrossRefGoogle Scholar
  9. 9.
    Zuo, W.M., et.al: Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier. In: Computers in Cardiology, pp. 253–256 (2008)Google Scholar
  10. 10.
    Uyar, A., Gurgen, F.: Arrhythmia classification using serial fusion of support vector machine and logistic regression. In: 4th IEEE workshop on intelligent data acquisition and advanced computing systems, pp. 560–565 (2007)Google Scholar
  11. 11.
    Elsayad, A.M.: Classification of ECG arrhythmia using Learning Vector Quantization Neural Networks. In: International Conference on Computer Engineering and Systems, pp. 139–144 (2009)Google Scholar
  12. 12.
    Polat, K., et al.: A new method to medical diagnosis; Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. J. Expert Syst. Appl. 31(2), 264–269 (2006)CrossRefGoogle Scholar
  13. 13.
    Lee, S.H., et al.: Extracting input features and fuzzy rules for detecting ECG arrhythmia based on NEWFM. In: International Conference on Intelligent and Advanced systems, pp. 22–25 (2007)Google Scholar
  14. 14.
    Jadhav, S., et al.: Modular neural network based arrhythmia classification system using ECG signal data. Int. J. Inf. Technol. Knowl. Manage. 4(1), 351–356 (2011)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.M.S. Ramaiah Institute of TechnologyBengaluruIndia
  2. 2.Dr. Ambedkar Institute of TechnologyBengaluruIndia

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