Skip to main content
Springer Nature Link
Log in

Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Parkinson’s disease is a neurological disorder that causes partial or complete loss of motor reflexes and speech and affects thinking, behavior, and other vital functions affecting the nervous system. Parkinson’s disease causes impaired speech and motor abilities (writing, balance, etc.) in about 90% of patients and is often seen in older people. Some signs (deterioration of vocal cords) in medical voice recordings from Parkinson’s patients are used to diagnose this disease. The database used in this study contains biomedical speech voice from 31 people of different age and sex related to this disease. The performance comparison of the machine learning algorithms k-Nearest Neighborhood (k-NN), Random Forest, Naive Bayes, and Support Vector Machine classifiers was performed with the used database. Moreover, the best classifier was determined for the diagnosis of Parkinson’s disease. Eleven different training and test data (45 × 55, 50 × 50, 55 × 45, 60 × 40, 65 × 35, 70 × 30, 75 × 25, 80 × 20, 85 × 15, 90 × 10, 95 × 5) were processed separately. The data obtained from these training and tests were compared with statistical measurements. The training results of the k-NN classification algorithm were generally 100% successful. The best test result was obtained from Random Forest classifier with 85.81%. All statistical results and measured values are given in detail in the experimental studies section.

Graphical abstract

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Jankovic J (2007) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79(4):368–376

    Article  Google Scholar 

  2. Langston JW (2002) Parkinson’s disease: current and future challenges. NeuroToxicology 23(4–5):443–450

    Article  PubMed  Google Scholar 

  3. O'Sullivan, S. B., & Schmitz, T. J. (2007). Physical rehabilitation (5th Edition b., Cilt Parkinson Disease). Philadelphia: F. A. Davis Company.

  4. de Rijk MC, Launer LJ, Berger K, Breteler MM, Dartigues JF, Baldereschi M et al (2000) Prevalence of Parkinson's disease in Europe: a collaborative study of population-based cohorts. Neurology 54:21–23

    Google Scholar 

  5. Parkinson Derneği. (2011). March 29, 2012 tarihinde Parkinson Nedir? http://www.parkinsondernegi.org/Icerik.aspx?Page=parkinsonnedir&ID=5

  6. de Lau LM, Breteler MM (2006) Epidemiology of Parkinson's disease. Lancet Neurol 5(6):525–535

    Article  PubMed  Google Scholar 

  7. Rajput M, Rajput A, Rajput AH (2007) Epidemiology. In: Pahwa R, Lyons (Dü) içinde KE (eds) Handbook of Parkinson’s disease (4 b.). Informa Healthcare, USA

    Google Scholar 

  8. Lang AE, Lozano AM (1998) Parkinson's disease - first of two parts. N Engl J Med 339:1044–1053

    Article  CAS  PubMed  Google Scholar 

  9. von Campenhausen S, Bornschein B, Wick R, Bötzel K, Sampaio C, Poewe W, Oertel W, Siebert U, Berger K, Dodel R (2005) Prevalence and incidence of Parkinson's disease in Europe. Eur Neuropsychopharmacol 15(4):473–490

    Article  Google Scholar 

  10. Schrag A, Ben-Schlomo Y, Quinn N (2002) How valid is the clinical diagnosis of Parkinson’s disease in the community? J Neurol Neurosurg Pshych 73:529–535

    Article  CAS  Google Scholar 

  11. Baldereschi M, Di Carlo A, Rocca WA, Vanni P, Maggi S, Perissinotto E et al (2000) Parkinson’s disease and parkinsonism in a longitudinal study. Two fold higher Incid Neurol 55:1358–1363

    CAS  Google Scholar 

  12. Haaxma CA, Bloem BR, Borm GF, Oyen WJ, Leenders KL, Eshuis S et al (2007) Gender differences in Parkinson’s disease. J Neurol Neurosurg Psychiatry 78:819–824

    Article  PubMed  Google Scholar 

  13. Elbaz A, Bower JH, Maraganore DM, McDonnell SK, Peterson BJ, Ahlskog JE, Schaid DJ, Rocca WA (2002) Risk tables for parkinsonism and Parkinson’s disease. J Clin Epidemiol 55:25–31

    Article  PubMed  Google Scholar 

  14. Singh N, Pillay V, Choonara YE (2007) Advances in the treatment of Parkinson's disease. Prog Neurobiol 81(1):29–44

    Article  CAS  PubMed  Google Scholar 

  15. Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Trans Biomed Eng 56(4):1010–1022

    Article  Google Scholar 

  16. National Collaborating Centre for Chronic Conditions (2006) Parkinson's disease. Royal College of Physicians, London

    Google Scholar 

  17. Darley FL, Aronson AE, Brown JR (1969) Differential diagnostic patterns of dysarthria. J Speech Hear Res 12:246–269

    Article  CAS  PubMed  Google Scholar 

  18. Gamboa J, Jimenez-Jimenez FJ, Nieto A, Montojo J, Orti-Pareja M, Molina JA et al (1997) Acoustic voice analysis in patients with Parkinson’s disease treated with dopaminergic drugs. J Voice 11:314–320

    Article  CAS  PubMed  Google Scholar 

  19. Ho A, Bradshaw JL, Iansek R (2008) For better or for worse: the effect of levodopa on speech in Parkinson’s disease. Mov Disord 23(4):574–580

    Article  PubMed  Google Scholar 

  20. Harel B, Cannizzaro M, Snyder PJ (2004) Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: a longitudinal case study. Brain Cogn 56:24–29

    Article  PubMed  Google Scholar 

  21. Skodda S, Rinsche H, Schlegel U (2009) Progression of dysprosody in Parkinson’s disease over time – a longitudinal study. Mov Disord 24(5):716–722

    Article  PubMed  Google Scholar 

  22. Sakar CO, Kursun O (2010) Telediagnosis of Parkinson’s disease using measurements of dysphonia. J Med Syst 34(4):591–599

    Article  PubMed  Google Scholar 

  23. Sapir S, Ramig L, Spielman J, Fox C (2010) Formant centralization ratio (FCR): a proposal for a new acoustic measure of dysarthric speech. J Speech Lang Hear Res 53:114–125

    Article  PubMed  Google Scholar 

  24. Cnockaert L, Schoentgen J, Auzou P, Ozsancak C, Defebve L, Grenez F (2008) Low frequency vocal modulations in vowels produced by Parkinsonian subjects. Speech Comm 50:288–300

    Article  Google Scholar 

  25. (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Informat 17(4):828–834

  26. Cunningham L, Mason S, Nugent C, Moore G, Finlay D, Craig D (2011) Home-based monitoring and assessment of Parkinson's disease. IEEE Transac Informat Technol Biomed 15(1):47–53

    Article  CAS  Google Scholar 

  27. Rigas G, Tzallas A, Tsipouras M, Bougia P, Tripoliti E, Baga DF et al (2012) Assessment of tremor activity in the Parkinson's disease using a set of wearable sensors. IEEE Trans Inf Technol Biomed 16(3):478–487

    Article  PubMed  Google Scholar 

  28. Marino S, Ciurleo R, Lorenzo G, Barresi M, De Salvo S, Giacoppo S et al (2012) Magnetic resonance imaging markers for early diagnosis of Parkinson's disease. Neural Regen Res 7(8):611–619

    PubMed  PubMed Central  Google Scholar 

  29. Dastgheib Z, Lithgow B, Moussavi Z (2012) Diagnosis of Parkinson's disease using electrovestibulography. Med Biol Eng Comput 50(3):483–491

    Article  CAS  PubMed  Google Scholar 

  30. Little MA, McSharry PE, Roberts SJ, Costello DA, Moroz IM (2007) Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed Eng Online 6(23):23

    Article  PubMed  PubMed Central  Google Scholar 

  31. Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO (2012) Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans Biomed Eng 59(5):1264–1271

    Article  PubMed  Google Scholar 

  32. Tsanas A, Little MA, McSharry PE, Ramige LO (2011) Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. J R Soc Interface 8:842–855

    Article  PubMed  Google Scholar 

  33. Tsanas, A., Little, M.A., Mcsharry, P.E. And Ramig, L.O. (2009). Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests. Nature Precedings

  34. Revett, K., Gorunescu, F. And Salem, A.B.M. (2009). Feature selection in Parkinson’s disease: a rough sets approach. Proceedings of the International Multiconference on Computer Science and Information Technology, Poland, 425–428

  35. Tsanas, A., Little, M.A., Mcsharry, P.E. And Ramıg, L.O. (2010). Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson's disease progression. IEEE international conference on acoustics speech and signal, 594–597

    Google Scholar 

  36. İsenkul ME (2011) Parkinson Hastalığı’nın Teşhisi İçin Veri Toplama ve Örüntü Tanıma Sistemi, Master Thesis. İstanbul Üniversitesi Fen Bilimleri Enstitüsü, İstanbul

    Google Scholar 

  37. Fahn S, And Przedborski S (2000) Parkinsonizm. In: Rowland LP (ed) Merritt’s textbook of neurology, vol 9789752771819, 10Th edn. Lippincott Williams & Wilkins, Philadelphia, pp 679–693

  38. Quinn N, And Critchley P, And Marsden CD (1987) Young onset Parkinson’s disease. Mov Disord 2(2):73–91

    Article  CAS  PubMed  Google Scholar 

  39. Gürüler H (2016) A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput & Applic 28(7):1657–1666. https://doi.org/10.1007/s00521-015-2142-2

    Article  Google Scholar 

  40. Hirschauer TJ, Adeli H, Buford JA (2015) Computer-aided diagnosis of Parkinson’s disease using enhanced probabilistic neural network. J Med Syst 39(11):179. https://doi.org/10.1007/s10916-015-0353-9

    Article  PubMed  Google Scholar 

  41. Devarajan M, Ravi L (2018) Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing. Multimed Tools Appl 78:32695–32719. https://doi.org/10.1007/s11042-018-6898-0

    Article  Google Scholar 

  42. Devarajan M, Ravi L (2018) Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing. Multimed Tools Appl 78:32695–32719. https://doi.org/10.1007/s11042-018-6898-0

    Article  Google Scholar 

  43. Kadam VJ, Jadhav SM (2018) Feature ensemble learning based on sparse autoencoders for diagnosis of Parkinson’s disease. In: Computing, Communication and Signal Processing, pp 567–581. https://doi.org/10.1007/978-981-13-1513-8_58

    Chapter  Google Scholar 

  44. Pradhan SD, Scherer R, Matsuoka Y, Kelly VE (2011) Use of sensitive devices to assess the effect of medication on attentional demands of precision and power grips in individuals with Parkinson disease. Med Biol Eng Comput 49:1195–1199. https://doi.org/10.1007/s11517-011-0798-z

    Article  PubMed  Google Scholar 

  45. Tan D, Pua Y, Balakrishnan S et al (2019) Automated analysis of gait and modified timed up and go using the Microsoft Kinect in people with Parkinson’s disease: associations with physical outcome measures. Med Biol Eng Comput 57:369–377. https://doi.org/10.1007/s11517-018-1868-2

    Article  PubMed  Google Scholar 

  46. Kallio M, Suominen K, Bianchi AM, Mäkikallio T, Haapaniemi T, Astafiev S, Sotaniemi KA, Myllylä VV, Tolonen U (2002) Comparison of heart rate variability analysis methods in patients with Parkinson's disease. Med Biol Eng Comput 40:408–414. https://doi.org/10.1007/BF02345073

    Article  CAS  PubMed  Google Scholar 

  47. Dastgheib ZA, Lithgow B, Moussavi Z (2012) Diagnosis of Parkinson’s disease using electrovestibulography. Med Biol Eng Comput 50:483–491. https://doi.org/10.1007/s11517-012-0890-z

    Article  CAS  PubMed  Google Scholar 

  48. Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, Hautbergue GM, Holbrook JD, Ferraiuolo L (2020) Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol 16:440–456. https://doi.org/10.1038/s41582-020-0377-8

    Article  PubMed  Google Scholar 

  49. Pio G, Ceci M, Prisciandaro F, Malerba D (2019) Exploiting causality in gene network reconstruction based on graph embedding. Mach Learn 109:1231–1279. https://doi.org/10.1007/s10994-019-05861-8

    Article  Google Scholar 

  50. Barracchia EP, Pio G, D’Elia D, Ceci M (2020) Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering. BMC Bioinformat 21(1):70. https://doi.org/10.1186/s12859-020-3392-2

    Article  CAS  Google Scholar 

  51. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  Google Scholar 

  52. Orhan U, Adem K (2012) The effects of probability factors in naive Bayes method. Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyumu, Bursa, pp 722–724

    Google Scholar 

  53. Krishna, P. R. and De, S. K., “Naive-Bayes classification using fuzzy approach”, Third International Conference on Intelligent Sensing and Information Processing, Bangalore/India, 61–64 (2005)

  54. https://www.saedsayad.com/support_vector_machine.htm, access date: 10 Nov 2018

Download references

Author information

Authors and Affiliations

  1. Department of Computer Technology, Aksaray University, Aksaray, Turkey

    Emre Avuçlu

  2. Department of Computer Technology, Karabuk University, Karabuk, Turkey

    Abdullah Elen

Authors
  1. Emre Avuçlu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Abdullah Elen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Emre Avuçlu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Avuçlu, E., Elen, A. Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements. Med Biol Eng Comput 58, 2775–2788 (2020). https://doi.org/10.1007/s11517-020-02260-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02260-3

Keywords