Niousha Karimi Dastjerd, Onur Can Sert, Tansel Ozyer and Reda Alhajj* Pages 1 - 23 ( 23 )
Together with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease. This paper contributes an attractive prediction framework based on some machine learning approaches. Several fuzzy classifiers have been employed in the process to distinguish people with Parkinsonism from healthy individuals. The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” available from the UCI repository. The results reported in this paper are better than the results reported by Sakar et al., where the same dataset was used, but with different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al.
Parkinson`s disease, data mining, machine learning, fuzzy classification, neuro fuzzy classification, adaptive neuro fuzzy classification
TOBB University of Economics and Technology, Sogutozu, Ankara, 06560, Department of Computer Science, University of Calgary, Calgary, Alberta, Department of Computer Science, University of Calgary, Calgary, Alberta, Department of Computer Science, University of Calgary, Calgary, Alberta