Skip to main content
SpringerLink
Log in

Feature Selection with Artificial Bee Colony Algorithms for Classifying Parkinson’s Diseases

  • Conference paper
  • First Online:
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

Abstract

Parkinson’s is a brain disease that affects the quality of human life significantly with very slow progresses. It is known that early diagnosis is of great importance to arrange relevant and efficient treatments. Data analytics and particularly predictive approaches such as machine learning techniques can be efficiently used for earlier diagonosis. As a typical big data problem, the number of features in the collected data of Parkinson’s symptoms per case matters crucially. It is known that the higher the number of features considered the more complexities incur in the handling algorithms. This leads to the dimensionality problem of datasets, which requires optimisation to overcome the trade-off between complexity and accuracy. In this study, artificial bee colony-based feature selection methods are employed in order to select the most prominent features for successful Parkinson’s Disease classification over the datasets. The optimised set of features were used in training and testing k nearest neigbourhood algorithm, and then verifed with support vector machine algorithm over the public dataset. This study demonstrates that binary versions of artificial bee colony algorithms can be significanlty successful in feature selection in comparison to the relevant literature.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, H., et al.: Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388(10053), 1459–1544 (2016)

    Article  Google Scholar 

  2. Oertel, W.H.: Recent advances in treating Parkinson’s disease. F1000Research, 6 Mar 2017

    Google Scholar 

  3. Massano, J., Bhatia, K.P.: Clinical approach to Parkinson’s disease: features, diagnosis, and principles of management. Cold Spring Harb. Perspect. Med. 2(6), a008870 (2012)

    Article  Google Scholar 

  4. Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed. Eng. OnLine 6(1), 1–19 (2007)

    Article  Google Scholar 

  5. 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 Trans. Biomed. Eng. 56(4), 1015–1022 (2009)

    Article  Google Scholar 

  6. Cai, Z., et al.: An intelligent Parkinson’s disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach. Comput. Math. Methods Med. 1–24, 2018 (2018)

    MATH  Google Scholar 

  7. Das, R.: A comparison of multiple classification methods for diagnosis of Parkinson’s disease. Expert Syst. Appl. 37(2), 1568–1572 (2010)

    Article  Google Scholar 

  8. Düğnci, M., Aydin, M.E.: A honeybees-inspired heuristic algorithm for numerical optimisation. Neural Comput. Appl. 1-15 (2019)

    Google Scholar 

  9. Li, D.C., Liu, C.W., Hu, S.C.: A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif. Intell. Med. 52(1), 45–52 (2011)

    Article  Google Scholar 

  10. Chen, H.L., Wang, G., Ma, C., Cai, Z.N., Liu, W.B., Wang, S.J.: An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 184, 131–144 (2016)

    Article  Google Scholar 

  11. Zuo, W.L., Wang, Z.Y., Liu, T., Chen, H.L.: Effective detection of Parkinson’s disease using an adaptive fuzzy k-nearest neighbor approach. Biomed. Signal Process. Control 8(4), 364–373 (2013)

    Article  Google Scholar 

  12. Gük, M.: An ensemble of k-nearest neighbours algorithm for detection of Parkinson’s disease. Int. J. Syst. Sci. 46(6), 1108–1112 (2015)

    Article  Google Scholar 

  13. Sakar, B.E., et al.: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inf. 17(4), 828–834 (2013)

    Article  Google Scholar 

  14. Zhang, H.-H., et al.: Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. BioMed. Eng. OnLine 15(1), 122 (2016)

    Article  Google Scholar 

  15. Abrol, V., Sharma, P., Sao, A.K.: Greedy dictionary learning for kernel sparse representation based classifier. Pattern Recogn. Lett. 78, 64–69 (2016)

    Article  Google Scholar 

  16. Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. In: Encyclopedia of Database Systems, pp. 532–538, Springer, Berlin (2009)

    Google Scholar 

  17. Sakar, C.O., et al.: A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl. Soft Comput. 74, 255–263 (2019)

    Article  Google Scholar 

  18. Cai, J., Luo, J., Wang, S., Yang, S.: Feature selection in machine learning: a new perspective. Neurocomputing 300, 70–79 (2018)

    Article  Google Scholar 

  19. Yusta, S.C.: Different metaheuristic strategies to solve the feature selection problem. Pattern Recogn. Lett. 30(5), 525–534 (2009)

    Article  Google Scholar 

  20. Nguyen, B.H., Xue, B., Zhang, M.: A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput. 54, 100663 (2020)

    Article  Google Scholar 

  21. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  22. Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)

    Article  Google Scholar 

  23. Kiran, M.S., Gunduz, M.: XOR-based artificial bee colony algorithm for binary optimization. Turk. J. Electr. Eng. Comput. Sci. 21, 2307–2328 (2013)

    Article  Google Scholar 

  24. Kashan, M.H., Nahavandi, N., Kashan, A.H.: DisABC: A new artificial bee colony algorithm for binary optimization. Appl. Soft Comput. 12(1), 342–352 (2012)

    Article  Google Scholar 

  25. Durgut, R.: Improved binary artificial bee colony algorithm. arXiv preprint arXiv:2003.11641

  26. Santana, C.J., Macedo, M., Siqueira, H., Gokhale, A., Bastos-Filho, C.J.A.: A novel binary artificial bee colony algorithm. Future Gener. Comput. Syst. 98, 180–196 (2019)

    Article  Google Scholar 

  27. Altay, E. V., Alatas, B. : Multi-objective association analysis of Parkinson disease with intelligent optimization algorithms. In: Proceedings of the 1st International Informatics and Software Engineering Conference (UBMYK), pp. 1–6, Ankara, Turkey (2019)

    Google Scholar 

  28. Badem, H., Turkusagi, D., Caliskan, A., Çil, Z.A.: Feature selection based on artificial bee colony for Parkinson disease diagnosis. In: Medical Technologies Congress (TIPTEKNO), İzmir. Turkey 2019, 1–4 (2019)

    Google Scholar 

  29. Tuncer, T., Dogan, S.: A novel octopus based Parkinson’s disease and gender recognition method using vowels. Appl. Acoust. 155, 75–83 (2019)

    Article  Google Scholar 

  30. Castro, C., Vargas-Viveros, E., Sanchez, A., Gutierrez-Lopez, E., Flores, D.L.: Parkinson’s disease classification using artificial neural networks. In: VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering, Cancun, Mexico, pp. 1060–1065 (2020)

    Google Scholar 

  31. Tuncer, T., Dogan, S., Acharya, U.R.: Automated detection of Parkinson’s disease using minimum average maximum tree and singular value decomposition method with vowels. Biocybern. Biomed. Eng. 40(1), 211–220 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing Engineering, Karabuk University, Karabuk, Turkey

    Rafet Durgut & Yusuf Yargı Baydilli

  2. Department of Computer Science and Creative Technologies, UWE Bristol, Bristol, UK

    Mehmet Emin Aydin

Authors
  1. Rafet Durgut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yusuf Yargı Baydilli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehmet Emin Aydin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehmet Emin Aydin .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Durgut, R., Baydilli, Y.Y., Aydin, M.E. (2020). Feature Selection with Artificial Bee Colony Algorithms for Classifying Parkinson’s Diseases. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-48791-1_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation