A Fuzzy Rule-Based Diagnosis of Parkinson’s Disease

  • D. KarunanithiEmail author
  • Paul Rodrigues
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Neurodegenerative brain disorder is the root cause of Parkinson’s Disease (PD). Neurodegenerative is the process of impairment of brain cells. PD is diagnosed through clinical methods. Hope this research work helps to identify the intensity of the PD. Fuzzy Inference System is used to identify the PD and its intensity. Fuzzy rules, Mamdani Fuzzy Inference, Membership Functions, and Defuzzification are the process used to obtain accurate results. Oxford Parkinson’s Disease Detection Dataset is used for this research work. Among 23 fields in the dataset, only four fields FoH, DFA, Spread1, and Spread2 are chosen for analyzing the PD diagnosis and intensity. These four fields values are categorized into three sets: one is PD affected subjects, the second set is common values for both PD affected subjects and healthy subjects, and the third set is completely healthy subjects. Intensity values are measured from low to maximum as 0–100. Eighty-one rules are framed to calculate the PD intensity. We hope this FIS model is a novel method for identifying the PD intensity and helps doctors to diagnose and treat the patients in an effective way.


Machine Learning Fuzzy Fuzzy Inference System Parkinson’s Disease 


  1. 1.
    Woo Y, Lee J, Hwang S, Hong CP (2013) Use of an adaptive-neuro fuzzy inference system to obtain the correspondance among balance, gait, and depression for Parkinson’s disease. J Korean Phys Soc 62(6):959–965CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Gelb D, Oliver E, Gilman S (1999) Diagnostic criteria for Parkinson disease. Arch Neurol 56(1):33–39. Scholar
  4. 4.
  5. 5.
    Pezard L, Jech R, RuÊzÏicÏka E (2001) Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson’s disease. Clin Neurophysiol 122:38–45CrossRefGoogle Scholar
  6. 6.
    Ene M (2008) Neural network-based approach to discriminate healthy people from those with Parkinson’s disease. Math Comput Sci Ser 35:112–116MathSciNetzbMATHGoogle Scholar
  7. 7.
    Caglar MF, Cetisli B, Toprak IB (2010) Automatic recognition of parkinson’s disease from sustained phonation tests using ANN and adaptive neuro-fuzzy classifier. J Eng Sci Des 1:59–64Google Scholar
  8. 8.
    Gil D, Johnson M (2009) Diagnosing Parkinson by using artificial neural networks and support vector machines. Glob J Comput Sci Technol 9:63–71Google Scholar
  9. 9.
    Duffy RJ (2005) Motor speech disorders: substrates, differential diagnosis and management, 2nd edn. Elsevier Mosby, St. LouisGoogle Scholar
  10. 10.
    Ho AK, Iansek R, Marigliani C, Bradshaw JL, Gates S (1998) Speech impairment in a large sample of patients with Parkinson’s disease. Behav Neurol 11:131–137. Scholar
  11. 11.
    Logemann JA, Fisher HB, Boshses B, Blonsky ER (1978) Frequency and co-occurrence of vocal-tract dysfunctions in speech of a large sample of Parkinson patients. J Speech Hear Disord 43:47–57CrossRefGoogle Scholar
  12. 12.
    Sapir S, Spielman JL, Ramig LO, Story BH, Fox C (2007) Effects of intensive voice treatment (the Lee Silverman Voice Treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: acoustic and perceptual findings. J Speech Lang Hear Res 50:899–912. Scholar
  13. 13.
    Center for Machine Learning and Intelligent Systems (2008).
  14. 14.
    Khezri R, Hosseini R, Mazinani M (2014) A fuzzy rule-based expert system for the prognosis of the risk of development of the breast cancer. Int J Eng (IJE) Trans Basics 27(10):1557–1564Google Scholar
  15. 15.
    Camara C, Warwick K, Bruña R, Aziz T, del Pozo F, Maestú F (2015) A fuzzy inference system for closed-loop deep brain stimulation in Parkinson’s disease. J Med Syst 39(11)Google Scholar
  16. 16.
    Hamidzadeh J, Javadzadeh R, Najafzadeh A (2015) Fuzzy rule based diagnostic system for detecting the lung cancer disease. J Renew Nat Resour Bhutan 3(1):147–157Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringManonmaniam Sundaranar UniversityTirunelveliIndia
  2. 2.Computer Science and EngineeringKing Khalid UniversityAbhaSaudi Arabia

Personalised recommendations