Australasian Conference on Artificial Life and Computational Intelligence

Artificial Life and Computational Intelligence pp 113-124 | Cite as

Parkinson’s Disease Data Classification Using Evolvable Wavelet Neural Networks

  • Maryam Mahsal Khan
  • Stephan K. Chalup
  • Alexandre Mendes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9592)

Abstract

Parkinson’s Disease is the second most common neurological condition in Australia. This paper develops and compares a new type of Wavelet Neural Network that is evolved via Cartesian Genetic Programming for classifying Parkinson’s Disease data based on speech signals. The classifier is trained using 10-fold and leave-one-subject-out cross validation testing strategies. The results indicate that the proposed algorithm can find high quality solutions and the associated features without requiring a separate feature pruning pre-processing step. The technique aims to become part of a future support tool for specialists in the early diagnosis of the disease reducing misdiagnosis and cost of treatment.

Keywords

Parkinson’s Disease Neuroevolution Wavelet neuralnetwork Cartesian genetic programming Artificial neural network 

Notes

Acknowledgments

We acknowledge Max Little, from the University of Oxford, UK, who created the database in collaboration with the National Center for Voice and Speech, Denver, Colorado, USA, who recorded the speech signals.

References

  1. 1.
    Parkinson’s Australia, Living with Parkinson’s disease; Deloitte Access Economics Pty Ltd, update - October 2011. http://www.parkinsonsnsw.org.au/. Accessed August 2014
  2. 2.
    Das, R.: A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst. Appl. 37(2), 1568–1572 (2010)CrossRefGoogle Scholar
  3. 3.
    Khan, M.M., Chalup, S.K., Mendes, A.: Evolving wavelet neural networks for breast cancer classification. In: 12th Australasian Data Mining Conference, Brisbane, Australia, 27th-28th November 2014Google Scholar
  4. 4.
    Lichman, M.: UCI machine learning repositry. http://archive.ics.uci.edu/ml
  5. 5.
    UCI machine learning repositry - Parkinsons data set. http://archive.ics.uci.edu/ml/datasets/Parkinsons.html. Accessed May 2014
  6. 6.
    Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A.E., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed. Eng. Online 6(1), 23 (2007)CrossRefGoogle Scholar
  7. 7.
    Sakar, C.O., Kursun, O.: Telediagnosis of Parkinson’s disease using measurements of dysphonia. J. Med. Syst. 34(4), 591–599 (2010)CrossRefGoogle Scholar
  8. 8.
    Ozcift, A.: Svm feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease. J. Med. Syst. 36(4), 2141–2147 (2012)CrossRefGoogle Scholar
  9. 9.
    Caglar, M.F., Cetisli, B., Toprak, I.B.: Automatic recognition of Parkinson’s disease from sustained phonation tests using ann and adaptive neuro-fuzzy classifier. J. Eng. Sci. Des. 2, 59–64 (2010)Google Scholar
  10. 10.
    Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J.L., Ramig, L.O.: Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 56(4), 1015–1022 (2009)CrossRefGoogle Scholar
  11. 11.
    Guo, P.-F., Bhattacharya, P., Kharma, N.: Advances in detecting Parkinson’s disease. In: Zhang, D., Sonka, M. (eds.) ICMB 2010. LNCS, vol. 6165, pp. 306–314. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  12. 12.
    Chen, H.-L., Huang, C.-C., Xin-Gang, Y., Xin, X., Sun, X., Wang, G., Wang, S.-J.: An efficient diagnosis system for detection of parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst. Appl. 40(1), 263–271 (2013)CrossRefGoogle Scholar
  13. 13.
    Polat, K.: Classification of parkinson’s disease using feature weighting method on the basis of fuzzy c-means clustering. Int. J. Syst. Sci. 43(4), 597–609 (2012)CrossRefMathSciNetMATHGoogle Scholar
  14. 14.
    Hariharan, M., Polat, K., Sindhu, R.: A new hybrid intelligent system for accurate detection of parkinson’s disease. Comput. Meth. Program. Biomed. 113(3), 904–913 (2014)CrossRefGoogle Scholar
  15. 15.
    Spadoto, A.A., Guido, R.C., Carnevali, F.L., Pagnin, A.F., Falcao, A.X., Papa, J.P.: Improving parkinson’s disease identification through evolutionary-based feature selection. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 7857–7860, August 2011Google Scholar
  16. 16.
    Alexandridis, A.K., Zapranis, A.D.: Wavelet neural networks: a practical guide. Neural Netw. 42, 1–27 (2013)CrossRefMATHGoogle Scholar
  17. 17.
    Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  18. 18.
    Khan, M.M., Khan, G.M., Ahmad, A.M., Miller, J.F.: Fast learning neural networks using cartesian genetic programming. Neurocomputing 121, 274–289 (2013)CrossRefGoogle Scholar
  19. 19.
    Miller, J.: What bloat? cartesian genetic programming on boolean problems. In: Genetic and Evolutionary computation Conference Late Breaking Papers, pp. 295–302. Morgan Kaufmann (2001)Google Scholar
  20. 20.
    Vassilev, V.K., Miller, J.F.: The advantages of landscape neutrality in digital circuit evolution. In: Miller, J.F., Thompson, A., Thompson, P., Fogarty, T.C. (eds.) ICES 2000. LNCS, vol. 1801, pp. 252–263. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  21. 21.
    Yu, T., Miller, J.F.: Neutrality and the evolvability of boolean function landscape. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 204–217. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  22. 22.
    Yu, T., Miller, J.F.: Finding needles in haystacks is not hard with neutrality. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 13–25. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  23. 23.
    Khan, M.M., Khan, G.M., Miller, J.F.: Efficient representation of recurrent neural networks for markovian/non-markovian non-linear control problems. In: International Conference on System Design and Applications (ISDA), pp. 615–620 (2010)Google Scholar
  24. 24.
    Walker, A., Miller, J.F.: Solving real-valued optimisation problems using cartesian genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO, pp. 1724–1730. ACM Press (2007)Google Scholar
  25. 25.
    Walker, J.A., Miller, J.F.: Predicting prime numbers using cartesian genetic programming. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 205–216. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  26. 26.
    Walker, J.A., Miller, J.F.: Changing the genospace: solving GA problems with cartesian genetic programming. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 261–270. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  27. 27.
    Beyer, H.G., Schwefel, H.P.: Evolution strategies: a comprehensive introduction. Natural Comput. 1(1), 3–52 (2002)CrossRefMathSciNetMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Maryam Mahsal Khan
    • 1
  • Stephan K. Chalup
    • 1
  • Alexandre Mendes
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceThe University of NewcastleCallaghanAustralia

Personalised recommendations