Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 253670, 10 pages
Discrimination between Alzheimer's Disease and Mild Cognitive Impairment Using SOM and PSO-SVM
1Department of Electrical Engineering, Chang Gung University, Tao-Yuan 333, Taiwan
2Department of Occupational Therapy, Bali Psychiatric Center, New Taipei City 249, Taiwan
3Department of Medical Imaging and Radiological Sciences, Chang Gung University, Tao-Yuan 333, Taiwan
4Department of Neuroscience, Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan
5Chang Gung Dementia Center, Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan
6Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan
7Department of Occupational Therapy, Chang Gung University, Tao-Yuan 333, Taiwan
Received 15 February 2013; Accepted 13 April 2013
Academic Editor: Chung-Ming Chen
Copyright © 2013 Shih-Ting Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape) from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA) to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM) and particle swarm optimization (PSO) for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM), were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.