title: Discrimination between Alzheimer's disease and mild cognitive impairment using SOM and PSO-SVM.
abstract: 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.
authors.affiliation: Department of Electrical Engineering, Chang Gung University, Tao-Yuan 333, Taiwan.
authors.forename: Shih-Ting
authors.initials: ST
authors.lastname: Yang
authors.forename: Jiann-Der
authors.initials: JD
authors.lastname: Lee
authors.forename: Tzyh-Chyang
authors.initials: TC
authors.lastname: Chang
authors.forename: Chung-Hsien
authors.initials: CH
authors.lastname: Huang
authors.forename: Jiun-Jie
authors.initials: JJ
authors.lastname: Wang
authors.forename: Wen-Chuin
authors.initials: WC
authors.lastname: Hsu
authors.forename: Hsiao-Lung
authors.initials: HL
authors.lastname: Chan
authors.forename: Yau-Yau
authors.initials: YY
authors.lastname: Wai
authors.forename: Kuan-Yi
authors.initials: KY
authors.lastname: Li
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification* / Q000175:diagnosis* / Q000473:pathology; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D016022:Case-Control Studies; D060825:Cognitive Dysfunction / Q000145:classification* / Q000175:diagnosis* / Q000473:pathology; D019295:Computational Biology; D057225:Data Mining; D003937:Diagnosis, Differential; D018450:Disease Progression; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D025341:Principal Component Analysis; D060388:Support Vector Machine