Fuzzy Classification Methods Based Diagnosis of Parkinson's disease from Speech Test Cases.

Fuzzy Classification Methods Based Diagnosis of Parkinson's disease from Speech Test Cases. Curr Aging Sci. 2019 Jun 25;: Authors: Dastjerd NK, Sert OC, Ozyer T, Alhajj R Abstract Together with Alzheimer, Parkinson's disease is considered as one of 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 some computing model which analyzes the data related to a given patient and predicts with acceptable accuracy where he/she will 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 recognize PWP (people with Parkinsonism) from healthy individuals. The fuzzy classified utilized in this study have been tested using the "Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set" available from UCI repository [1]. The results reported by the study described in this paper are better than the results reported in [1], where the same dataset was used but with different classifiers. This demonstrate the applicability and effectiveness of fuzzy classification as compared to non-fuzzy classifiers used in [1]. PMID: 31241024 [PubMed - as supplied by publisher]
Source: Current Aging Science - Category: Geriatrics Tags: Curr Aging Sci Source Type: research