Detection of Parkinson’s Disease Using Fuzzy Inference System

  • Atanu Chakraborty
  • Aruna Chakraborty
  • Bhaskar Mukherjee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 384)


Parkinson’s disease is a degenerative neurological disorder which severely affects the ability to move, speak and behave. Parkinsonism is clinically diagnosed but the laboratory confirmation is hard to obtain and needs very sophisticated investigations with high economic burden. In this work a fuzzy based approach is adapted for efficient and proper detection of Parkinson’s disease using biomedical measurements of voice which is cheap and cost effective. The proposed system can also be utilised as a supplementary test to confirm the clinical diagnosis of Parkinson’s disease in a remote place using the telephony system to track the voice signal. Moreover, it can be used as a guide for the treatment of Parkinson’s disease as it gives a quantitative measure to signify the extent of the disease. The model is constructed using a Sugeno-Takagi Fuzzy Inference System (FIS) based on Fuzzy C-Means clustering. The result shows that the detection accuracy of the system is up to 96-97 percent with a reasonable efficiency. The study also compares the results with the Subtractive Clustering based Fuzzy Inference System. The accuracy of the Fuzzy C-Means based Fuzzy Inference System is found to be higher than the other one.


Parkinson’s disease Fuzzy C-Means Sugeno-Takagi Fuzzy Inference System 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Atanu Chakraborty
    • 1
  • Aruna Chakraborty
    • 1
  • Bhaskar Mukherjee
    • 2
    • 3
  1. 1.Department of Computer Science and EngineeringSt. Thomas’ College of Engineering & TechnologyKolkataIndia
  2. 2.Department of PsychiatryMalda Medical College & HospitalMaldaIndia
  3. 3.Senior Consultant PsychiatricAntara Psychiatric HospitalKolkataIndia

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