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Computational Intelligence, Cyber Security and Computational Models pp 247–259Cite as

Comparison of Machine Learning Techniques for the Identification of the Stages of Parkinson’s Disease

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 412)

Abstract

Parkinson’s Disease (PD) is a degenerative disease of the central nervous system. This work performs a four-class classification using the motor assessments of subjects obtained from the Parkinson’s Progressive Markers Initiative (PPMI) database and a variety of techniques like Deep Neural Network (DNN), Support Vector Machine (SVM), Deep Belief Network (DBN) etc. The effect of using feature selection was also studied. Due to the skewness of the data, while evaluating the performance of the classifier, along with accuracy other metrics like precision, recall and F1-score were also used. The best classification performance was obtained when a feature selection technique based on Joint Mutual Information (JMI) was used for selecting the features that were then used as input to the classification algorithm like SVM. Such a combination of SVM and feature selection algorithm based on JMI yielded an average classification accuracy of 87.34 % and an F1-score of 0.84.

Keywords

  • PPMI
  • Motor assessments
  • Multi-class classification
  • Feature selection
  • SVM
  • DNN
  • Dropout
  • DBN

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Acknowledgments

PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at http://www.ppmi-info.org/fundingpartners].

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Karunya University, Coimbatore, India

    P. F. Deena & Kumudha Raimond

Authors
  1. P. F. Deena
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  2. Kumudha Raimond
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Corresponding author

Correspondence to P. F. Deena .

Editor information

Editors and Affiliations

  1. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Muthukrishnan Senthilkumar

  2. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Vijayalakshmi Ramasamy

  3. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Shina Sheen

  4. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    C. Veeramani

  5. Department of Mathematics, Ryerson University, TORONTO, Ontario, Canada

    Anthony Bonato

  6. Fac. Science&Technology, Deakin University, Burwood, Australia

    Lynn Batten

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Deena, P.F., Raimond, K. (2016). Comparison of Machine Learning Techniques for the Identification of the Stages of Parkinson’s Disease. In: Senthilkumar, M., Ramasamy, V., Sheen, S., Veeramani, C., Bonato, A., Batten, L. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 412. Springer, Singapore. https://doi.org/10.1007/978-981-10-0251-9_25

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  • DOI: https://doi.org/10.1007/978-981-10-0251-9_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0250-2

  • Online ISBN: 978-981-10-0251-9

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