title: A hybrid manifold learning algorithm for the diagnosis and prognostication of Alzheimer's disease.
abstract: The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. Such data are difficult to compare, visualize, and analyze due to the heterogeneous nature of medical tests. We present a hybrid manifold learning framework, which embeds the feature vectors in a subspace preserving the underlying pairwise similarity structure, i.e. similar/dissimilar pairs. Evaluation tests are carried out using the neuroimaging and biological data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in a three-class (normal, mild cognitive impairment, and AD) classification task using support vector machine (SVM). Furthermore, we make extensive comparison with standard manifold learning algorithms, such as Principal Component Analysis (PCA), Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and isometric feature mapping (Isomap). Experimental results show that our proposed algorithm yields an overall accuracy of 85.33% in the three-class task.
authors.affiliation: Department of Computer Science, The University of Western Ontario, London, ON, Canada; Robarts Research, London, ON, Canada.
authors.forename: Peng
authors.initials: P
authors.lastname: Dai
authors.affiliation: Department of Computer Science, The University of Western Ontario, London, ON, Canada; Lawson Health Research Institute, St. Josephs Healthcare, London, ON, Canada.
authors.forename: Femida
authors.initials: F
authors.lastname: Gwadry-Sridhar
authors.affiliation: Department of Computer Science, The University of Western Ontario, London, ON, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Bauer
authors.affiliation: Division of Geriatric Medicine, University of Western Ontario, London, ON, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Borrie
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000000981:diagnostic imaging; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008954:Models, Biological; D017145:Models, Educational; D059906:Neuroimaging; D025341:Principal Component Analysis; D011379:Prognosis; D060388:Support Vector Machine