A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease

• Published in: Neurocomputing (Amsterdam) Vol. 320; pp. 195 - 202
• Main Authors: Zeng, Nianyin, Qiu, Hong, Wang, Zidong, Liu, Weibo, Zhang, Hong, Li, Yurong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 03.12.2018
• Subjects: Alzheimer’s disease; Classification; Principal component analysis; Support vector machine; Switching delayed particle swarm optimization; Support vector machine; Alzheimer’s disease; Switching delayed particle swarm optimization; Classification; Principal component analysis
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2018.09.001

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.