A fuzzy expert systems design for diagnosis of Parkinson's disease

This paper delineates a model of a fuzzy expert system, dedicated to Parkinson's disease diagnosis. This has been carried out in the view of improving the diagnosis currently established upon basis of subjective scores. Parkinson's disease has proven a high incidence within third age people, signifying the second neurodegenerative disease, after Alzheimer. This paper proposes a new quantitative evaluation and analysis system for patients, in order to diagnose the Parkinson's disease at the incipient stage. The input parameters of the system are represented by amplitude, frequency, the spectral character and trembling localization. The last one signifies the main symptom that occurs in Parkinson's disease, but others can also be mentioned: small handwriting, loss of smell, trouble sleeping, soft or low voice. The proposed system proves to be fast, efficient, and non-invasive and can be used by both physicians and patients at home, as well.