Utilization of Data Analytics-Based Approaches for Hassle-Free Prediction Parkinson Disease

  • S. Jeba PriyaEmail author
  • G. Naveen Sundar
  • D. Narmadha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)


Individuals with Parkinson’s disease don’t have a sufficient substance called dopamine since a few nerves in the brain lose their functionality. Individuals with Parkinson’s disease are in deceptive and damaging condition . Diagnosing this disease on the basis of the motor and cognitive shortage is extremely critical. Machine learning approaches are utilized to settle on prescient choices via preparing the machines to learn with the trained information. It assumes a fundamental role in foreseeing Parkinson’s disease in its beginning periods. In this paper, our primary goal is to build up an advanced algorithm to accomplish good classification accuracy utilizing data mining techniques. In this procedure, we distinguish some current algorithms (e.g., Naïve Bayes, decision tree, discriminant, and random forest) and its execution is broken down. Result acquired through these grouping algorithms is moderately prescient. During the time spent in the computation of these algorithms, Naïve Bayes can construct the framework with the high precision rate of 94.11%.


Parkinson’s disease Naïve Bayes Decision tree Random forest Classification Regression 


  1. 1.
    Parkinson’s Australia: Parkinson’s—description, incidence and theories of causation.
  2. 2.
    Blonder, L.X., Gur, R.E., Gura, R.C.: The effects of right and left hemiparkinsonism on prosody. Brain Lang. 36, 193–207 (1989)CrossRefGoogle Scholar
  3. 3.
    Ariatti, A., Benuzzi, F., Nichelli, P.: Recognition of emotions from visual and prosodic cues in Parkinson’s disease. Neurol. Sci. 29, 219–227 (2008)CrossRefGoogle Scholar
  4. 4.
    Dara, C., Monetta, L., Pell, M.D.: Vocal emotion processing in Parkinson’s disease: reduced sensitivity to negative emotions. Brain Res. 1188, 100–111 (2008)CrossRefGoogle Scholar
  5. 5.
    Fearnley, J.M., Lees, A.J.: Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain. 114(5), 2283–2301 (1991)CrossRefGoogle Scholar
  6. 6.
    Kalman, Y.M.: HCI markers: a conceptual framework for using human-computer interaction data to detect disease processes. In: The 6th mediterranean conference on information systems (MCIS), Limassol, Cyprus (2011)Google Scholar
  7. 7.
    Smith, M.E., Ramig, L.O., Dromey, C., Perez, K.S., Samandari, R.: Intensive voice treatment in Parkinson disease: laryngostroboscopic findings. J. Voice 9(4), 453–459 (1995)CrossRefGoogle Scholar
  8. 8.
    Marinelli, L., Quartarone, A., Hallet, M., Ghilardi, M.F.: The many facts of motor learning and their relevance for Parkinson’s diseases. J. Clin. Nerophysiology 128(7), 1127–1141 (2017)CrossRefGoogle Scholar
  9. 9.
    Kotsavasiloglou, C., Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M.: Machine learning-based classification of simple drawing movements in Parkinson’s disease. J. Biomed. Signal Process. Control 31, 174–180 (2017)CrossRefGoogle Scholar
  10. 10.
    Nilashi, M., bin Ibrahim, O., et al.: An analytical method for diseases prediction using machine learning techniques. J. Comput. Chem. Eng. 106(2), 212–223 (2017)CrossRefGoogle Scholar
  11. 11.
    Nilashi, M., et al.: A hybrid intelligent system for the prediction of Parkinson’s disease progression using machine learning techniques. J. Biocybern. Biomed. Eng. 38(1), 1–15 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • S. Jeba Priya
    • 1
    Email author
  • G. Naveen Sundar
    • 1
  • D. Narmadha
    • 1
  1. 1.CSE DepartmentKITSCoimbatoreIndia

Personalised recommendations