A Projection Based Learning Meta-cognitive RBF Network Classifier for Effective Diagnosis of Parkinson’s Disease

  • G. Sateesh Babu
  • S. Suresh
  • K. Uma Sangumathi
  • H. J. Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)


In this paper, we proposed a ‘Projection Based Learning for Meta-cognitive Radial Basis Function Network (PBL-McRBFN)’ classifier for effective diagnosis of Parkinson’s disease. McRBFN is inspired by human meta-cognitive learning principles. McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, network parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. The experimental results on parkinson’s data sets based on vocal and gait features clearly highlight the superior performance of PBL-McRBFN classifier over results reported in the literature for detection of individual with or without PD.


Extreme Learn Machine Hide Neuron Radial Basis Function Neural Network Radial Basis Function Network Cognitive Component 
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  1. 1.
    Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J., Ramig, L.O.: Suitability of Dysphonia Measurements for Telemonitoring of Parkinson’s Disease. IEEE Transactions on Biomedical Engineering 56(4), 1015–1022 (2009)CrossRefGoogle Scholar
  2. 2.
    Strom, F., Koker, R.: A parallel neural network approach to prediction of Parkinson’s Disease. Expert Systems with Applications 38(10), 12470–12474 (2011)CrossRefGoogle Scholar
  3. 3.
    Das, R.: A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications 37(2), 1568–1572 (2010)CrossRefGoogle Scholar
  4. 4.
    Tahir, N.M., Manap, H.H.: Parkinson Disease Gait Classification based on Machine Learning Approach. Journal of Applied Sciences 12(2), 180–185 (2012)CrossRefGoogle Scholar
  5. 5.
    Jeon, H.S., Han, J., Yi, W.J., Jeon, B., Park, K.S.: Classification of Parkinson gait and normal gait using Spatial-Temporal Image of Plantar pressure. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4672–4675 (2008)Google Scholar
  6. 6.
    Wenden, A.L.: Metacognitive knowledge and language learning. Applied Linguistics 19(4), 515–537 (1998)CrossRefGoogle Scholar
  7. 7.
    Rivers, W.P.: Autonomy at All costs: An Ethnography of Metacognitive Self-Assessment and Self-Management among Experienced Language Learners. The Modern Language Journal 85(2), 279–290 (2001)CrossRefGoogle Scholar
  8. 8.
    Isaacson, R., Fujita, F.: Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning. Journal of the Scholarship of Teaching and Learning 6(1), 39–55 (2006)Google Scholar
  9. 9.
    Suresh, S., Dong, K., Kim, H.J.: A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16-18), 3012–3019 (2010)CrossRefGoogle Scholar
  10. 10.
    Suresh, S., Savitha, R., Sundararajan, N.: A Sequential Learning Algorithm for Complex-valued Self-regulating Resource Allocation Network-CSRAN. IEEE Transactions on Neural Networks 22(7), 1061–1072 (2011)CrossRefGoogle Scholar
  11. 11.
    Sateesh Babu, G., Suresh, S.: Meta-cognitive Neural Network for classification problems in a sequential learning framework. Neurocomputing 81, 86–96 (2012)CrossRefGoogle Scholar
  12. 12.
    Savitha, R., Suresh, S., Sundararajan, N.: Metacognitive learning in a Fully Complex-valued Radial Basis Function Neural Network. Neural Computation 24(5), 1297–1328 (2012)CrossRefGoogle Scholar
  13. 13.
    Suresh, S., Subramanian, K.: A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems. In: The International Joint Conference on Neural Networks (IJCNN), pp. 2507–2512 (2011)Google Scholar
  14. 14.
    Suresh, S., Sundararajan, N., Saratchandran, P.: Risk-sensitive loss functions for sparse multi-category classification problems. Information Sciences 178(12), 2621–2638 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Hausdorff, J.M., Lowenthal, J., Herman, T., Gruendlinger, L., Peretz, C., Giladi, N.: Rhythmic auditory stimulation modulates gait variability in Parkinson’s disease. European Journal of Neuroscience 26(8), 2369–2375 (2007)CrossRefGoogle Scholar
  16. 16.
    Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(2), 513–529 (2012)CrossRefGoogle Scholar
  17. 17.
    Suresh, S., Sundararajan, N., Saratchandran, P.: A sequential multi-category classifier using radial basis function networks. Neurocomputing 71(7-9), 1345–1358 (2008)CrossRefGoogle Scholar
  18. 18.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011) software available at,

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • G. Sateesh Babu
    • 1
  • S. Suresh
    • 1
  • K. Uma Sangumathi
    • 1
  • H. J. Kim
    • 2
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.CISTKorea UniversitySeoulKorea

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