期刊
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
卷 2018, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2018/2396952
关键词
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资金
- National Natural Science Foundation of China [61702376, 61402337]
- Zhejiang Provincial Natural Science Foundation of China [LY17F020012, LY14F020035, LQ13F020011, LQ13G010007]
- Science and Technology Plan Project of Wenzhou of China [ZG2017019, H20110003, Y20160070, Y20160469]
Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vectormachine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.
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