Detection of Parkinson’s Disease Using Fuzzy Inference System
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.
KeywordParkinson’s disease Fuzzy C-Means Sugeno-Takagi Fuzzy Inference System
Unable to display preview. Download preview PDF.
- 1.Murdoch, B., Whitehill, T., de Letter, M., Jones, H.: Communication Impairments in Parkinson’s Disease. SAGE-Hindawi Access to Research, Parkinson’s disease 2011, 1–2 (2011)Google Scholar
- 3.Tjaden, K.: Speech and Swallowing in Parkinson’s Disease. Topics in Geriatric Rehabilitation, 115–126 (2008)Google Scholar
- 5.Raju, G., Thomas, B., Tobgay, S., Kumar, T.S.: Fuzzy clustering methods in data mining: a comparative case analysis. In: ICACTE, pp. 489–493. IEEE publication (2008)Google Scholar
- 9.Chiu, S.L.: Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy Systems 2, 267–278 (1994)Google Scholar
- 10.Mitra, R., Kumar, V.: Identification of rules using subtractive clustering with application to fuzzy controllers, vol. 7, pp. 4125–4130. IEEE publications (2004)Google Scholar
- 11.Kaur, A., Kaur, A.: Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System. International Journal of Soft Computing and Engineering (IJSCE) 2, 323–325 (2012)Google Scholar
- 12.Popescu, M., Khalilia, M.: Improving disease prediction using ICD-9 ontological features, pp. 1805–1809. IEEE publications (2011)Google Scholar
- 13.Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A.E., Moroz, I.M.: Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection. BioMedical Engineering OnLine 6 (2007)Google Scholar