Paper Info
Title
Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach.
Abstract
In this paper, we present an effective and efficient diagnosis system based on particle swarm optimization (PSO) enhanced fuzzy k-nearest neighbor (FKNN) for Parkinson's disease (PD) diagnosis. In the proposed system, named PSO-FKNN, both the continuous version and binary version of PSO were used to perform the parameter optimization and feature selection simultaneously. On the one hand, the neighborhood size k and the fuzzy strength parameter ruin FKNN classifier are adaptively specified by the continuous PSO. On the other hand, binary PSO is utilized to choose the most discriminative subset of features for prediction. The effectiveness of the PSO-FKNN model has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the receiver operating characteristic (ROC) curve (AUC). Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation analysis, with the mean accuracy of 97.47%. Promisingly, the proposed diagnosis system might serve as a new candidate of powerful tools for diagnosing PD with excellent performance. (C) 2013 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2013
10.1016/j.bspc.2013.02.006
Biomedical Signal Processing and Control
Keywords
Field
DocType
Fuzzy k-nearest neighbor method,Particle swarm optimization,Feature selection,Medical diagnosis,Parkinson's disease
k-nearest neighbors algorithm,Particle swarm optimization,Receiver operating characteristic,Pattern recognition,Feature selection,Fuzzy logic,Artificial intelligence,Classifier (linguistics),Discriminative model,Mathematics,Binary number
Journal
Volume
Issue
ISSN
8
4
1746-8094
Citations 
PageRank 
References 
20
0.77
23
Authors
4
Name
Order
Citations
PageRank
Wanli Zuo134242.73
Zhiyan Wang2407.88
Tong Liu34712.77
Hui-Ling Chen465526.09