Paper Info
Title
A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease.
Abstract
In healthcare sector, it is of crucial importance to accurately diagnose Alzheimer’s disease (AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration and provide early treatment for AD patients. In this paper, a framework is proposed for the diagnosis of AD, which consists of MRI images preprocessing, feature extraction, principal component analysis, and the support vector machine (SVM) model. In particular, a new switching delayed particle swarm optimization (SDPSO) algorithm is proposed to optimize the SVM parameters. The developed framework based on the SDPSO-SVM model is successfully applied to the classification of AD and MCI using MRI scans from ADNI dataset. Our developed algorithm can achieve excellent classification accuracies for 6 typical cases. Furthermore, experiment results demonstrate that the proposed algorithm outperforms several SVM models and also two other state-of-art methods with deep learning embedded, thereby serving as an effective AD diagnosis method.
Year
DOI
Venue
2018
10.1016/j.neucom.2018.09.001
Neurocomputing
Keywords
Field
DocType
Alzheimer’s disease,Support vector machine,Switching delayed particle swarm optimization,Classification,Principal component analysis
Particle swarm optimization,Pattern recognition,Support vector machine,Algorithm,Feature extraction,Preprocessor,Artificial intelligence,Deep learning,Machine learning,Mathematics,Principal component analysis,Cognitive impairment
Journal
Volume
ISSN
Citations 
320
0925-2312
12
PageRank 
References 
Authors
0.52
27
6
Name
Order
Citations
PageRank
Nianyin Zeng138412.14
Hong Qiu2120.52
Zidong Wang311003578.11
Weibo Liu452016.88
Hong Zhang527626.98
Yurong Li6120.52