Abstract
Several classification methods have been proposed for assisting computer-aided diagnosis of Alzheimer’s disease (AD). Among them, classification methods including (i) support vector machines (SVM), and (ii) generalized multiple kernel learning (GMKL) are getting increasing attention in recent studies. Nevertheless, there is little research on the comparison among these methods to find a better classification framework and further analysis of brain imaging features in the study of AD. To deal with this issue, we carry out exhaustive comparative study in this work to evaluate efficiency of these different classification methods. For the experiments, we used FreeSurfer mean cortical thickness dataset downloaded from the ADNI database (adni.loni.usc.edu) baseline data. The classification accuracy (in classifying the three classes CN, LMCI, AD) of comparative methods has been evaluated using 3-fold cross validation. From the comparative study, we could observe that GMKL is the most promising framework if the sufficient training data can be provided.
Data used in preparation of this article were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Aknowledgement_List.pdf
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Acknowledgments
This paper was supported by the National Research Foundation (NRF) grant funded by the Korean government (NRF-2013R1A1A2012543,NRF-2014M3C7A1046050).
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Mallak, A., Gwak, J., Song, JI., Lee, SW., for the Alzheimer’s Disease Neuroimaging initiative. (2016). Computational and Comparative Study on Multiple Kernel Learning Approaches for the Classification Problem of Alzheimer’s Disease. In: Vinh, P., Barolli, L. (eds) Nature of Computation and Communication. ICTCC 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 168. Springer, Cham. https://doi.org/10.1007/978-3-319-46909-6_30
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DOI: https://doi.org/10.1007/978-3-319-46909-6_30
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