Skip to main content
SpringerLink
Log in

Performance Analysis of Different Classifiers for Tele-Diagnosis of Parkinson’s Disease

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Parkinson’s disease (PD) is a second most progressive neurodegenerative disorder. Millions of people across the world are affected with this disease. In recent days, there are significant research has been reported for the screening of PD using Dysphonia features. In this study, a new weights generation method named as Kernel Fuzzy C-means Ratio based on different clustering technique (KFCM, FCM and KCM) has been proposed. The main aim of this work is to transform non-separable speech features in the dataset to a linearly separable such that the classification can be enhanced. In classification stage, six different classifiers are used to classify the weighted data and significant improvement in sensitivity, accuracy and specificity parameters are recorded.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Backman, W., Bendel, D., & Rakhit, R. (2010). The telecardiology revolution: Improving the management of cardiac disease in primary care. Journal of the Royal Society of Medicine, 103(11), 442–446.

    Article  Google Scholar 

  2. McLean, S., Chandler, D., Nurmatov, U., Liu, J., Pagliari, C., Car, J., & Sheikh, A. (2011). Telehealthcare for asthma: A Cochrane review. CMAJ, 183(11), 733–742.

    Article  Google Scholar 

  3. Johnson, N. D. (2010). Teleradiology 2010: Technical and organizational issues. Pediatric Radiology, 40(6), 1052–1055.

    Article  Google Scholar 

  4. Evans, A. J., Kiehl, T.-R., & Croul, S. (2010). Frequently asked questions concerning the use of whole-slide imaging telepathology for neuropathology frozen sections. Seminars in Diagnostic Pathology, 27(3), 160–166.

    Article  Google Scholar 

  5. Demaerschalk, B. M. (2010). Telestrokologists: Treating stroke patients here, there, and everywhere with telemedicine. Seminars in Neurology, 30(05), 477–491.

    Article  Google Scholar 

  6. Booij, J., & Knol, R. J. J. (2007). SPECT imaging of the dopaminergic system in (premotor) Parkinson’s disease. Parkinsonism & Related Disorders, 13(SUPPL. 3), 425–428.

    Article  Google Scholar 

  7. Cummings, J. L., Henchcliffe, C., Schaier, S., Simuni, T., Waxman, A., & Kemp, P. (2011). The role of dopaminergic imaging in patients with symptoms of dopaminergic system neurodegeneration. Brain, 134(11), 3146–3166.

    Article  Google Scholar 

  8. Booij, J., Tissingh, G., Boer, G. J., Speelman, J. D., Stoof, J. C., Janssen, A. G., Wolters, E. C., & van Royen, E. A. (1997). [123I]FP-CIT SPECT shows a pronounced decline of striatal dopamine transporter labelling in early and advanced Parkinson’s disease. Journal of Neurology, Neurosurgery and Psychiatry, 62(2), 133–140.

    Article  Google Scholar 

  9. Nussbaum, R. L., & Ellis, C. E. (2003). Alzheimer’s disease and Parkinson’s disease. Alzheimer’s Disease Parkinson’s Disease, 348(14), 1356–1364.

    Google Scholar 

  10. Parkinson Study Group and Parkinson Study Group. (2002). Dopamine transporter bain imaging to assess the effects of pramipexole vs levodopa on Parkinson disease progression. JAMA, 287(13), 1653–1661.

    Article  Google Scholar 

  11. Mühlhaus, J., Frieg, H., Bilda, K., & Ritterfeld, U. (2017) Game-based speech rehabilitation for people with Parkinson's disease. In International conference on universal access in human-computer interaction, UAHCI 2017, Part III, LNCS 10279 (pp. 76–85). https://doi.org/10.1007/978-3-319-58700-4_7

  12. Little, M. A., Mcsharry, P. E., Hunter, E. J., Spielman, J., & Ramig, L. O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. Nature Precedings, 56(4), 1015–1022.

    Google Scholar 

  13. Geman, O. (2011). Data processing for Parkinson’s disease: Tremor, speech and gait signal analysis. In International conference E-health and bioengineering (pp. 24–27).

  14. Tsanas, A., Little, M. A., Mcsharry, P. E., Member, S., Spielman, J., & Ramig, L. O. (2012). Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 59(5), 1264–1271.

    Article  Google Scholar 

  15. Prashanth, R., Roy, S. D., Mandal, P. K., & Ghosh, S. (2014). Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging. Expert Systems with Applications, 41, 3333–3342.

    Article  Google Scholar 

  16. Lee, P. W. H., Wang, Z. J., Palmer, S. J., Mckeown, M. J., & Preprocessing, A. (2007). Spectral clustering of fMRI data within regions of interest: Clarification of L-dopa effects in Parkinson’s disease29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5235–5238).

  17. Bhattacharya, I. (2010). SVM classification to distinguish parkinson disease patients. In Proceedings 1st Amrita ACM-W celebration women computing in India. ACM.

  18. Ozcift, A. (2012). SVM feature selection based rotation forest ensemble classifiers to improve computer-Aided diagnosis of Parkinson disease. Journal of Medical Systems, 36(4), 2141–2147.

    Article  Google Scholar 

  19. Khemphila, A., & Boonjing, V. (2012). Parkinsons disease classification using neural network and feature selection. International Journal of Mathematical and Computational Sciences, 6(4), 377–380.

    Google Scholar 

  20. Ozkan, H. (2016). A comparison of classification methods for telediagnosis of Parkinson’s disease. Entropy, 18, 115.

    Article  Google Scholar 

  21. Acevedo, E., Acevedo, A., & Felipe, F. (2011). Associative memory approach for the diagnosis of Parkinson’s disease. In Mexican conference on pattern recognition (pp. 103–117). Springer.

  22. Viswanathan, R., Arjunan, S. P., Bingham, A., Jelfs, B., Kempster, P., Raghav, S., & Kumar, D. K. (2020). Complexity measures of voice recordings as a discriminative tool for Parkinson’s disease. Biosensors, 10(1), 1.

    Article  Google Scholar 

  23. Cai, Z., Gu, J., Wen, C., Zhao, D., Huang, C., Huang, H., Tong, C., Li, J., & Chen, H. (2018). An intelligent Parkinson’s disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach. Computational and Mathematical Methods in Medicine, 2021, 1–18.

    Article  Google Scholar 

  24. Dao-qiang Zhang, S. C. (2003). Kernel-based fuzzy and possibilistic C-means clustering. In International conference on artificial neural networks (pp. 122–125).

  25. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy C-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.

    Article  Google Scholar 

  26. Pal, N. R., Pal, K., Keller, J. M., & Bezdek, J. C. (2005). A possibilistic fuzzy C-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 13(4), 517–530.

    Article  Google Scholar 

  27. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892.

    Article  Google Scholar 

  28. Yuan, C., & Haitao, Y. (2019). Research on K-value selection method of K-means clustering algorithm. Multidiscipline Scientific Journal, 2(2), 226–235.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India

    Vijay Khare & Manju Singh

Authors
  1. Vijay Khare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manju Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vijay Khare.

Ethics declarations

Conflict of interest

We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. We know of no conflicts of interest associated with this publication, and there has been no significant financial support for this work that could have influenced its outcome. As Corresponding Author, I confirm that the manuscript has been read and approved for submission by all the named authors. We have used well known available standard data set. So ethical approval was not required for this study. Because we have not collected new data or perform any experiment for this study. All authors approved the final version of the manuscript and agree to be accountable for all aspects of this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khare, V., Singh, M. Performance Analysis of Different Classifiers for Tele-Diagnosis of Parkinson’s Disease. Wireless Pers Commun 122, 331–348 (2022). https://doi.org/10.1007/s11277-021-08901-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08901-6

Keywords

Search

Navigation