6th International Young Scientist Congress (IYSC-2020) will be Postponed to 8th and 9th May 2021 Due to COVID-19. 10th International Science Congress (ISC-2020).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Alzheimer's disease Detection using Data Mining Techniques, MRI Imaging, Blood-Based Biomarkers and Neuropsychological tests

Author Affiliations

  • 1Department of Computer Engineering, Islamic Azad University South Tehran Branch,P.O. Box 11365/4435, IRAN
  • 2 Department of Applied Mathematics, Islamic Azad University South Tehran Branch,P.O. Box 11365/4435, IRAN

Res. J. Recent Sci., Volume 4, Issue (4), Pages 32-37, April,2 (2015)


Finding a cure for Alzheimer's disease has been facing many challenges due to the lack of reliable biomarkers for detection and prediction of risk. Fluid based biomarkers provide some criteria for identification of the disease's current stage in patients. But these markers are not reliable predictors for disease progression or response to treatment; also most of these markers are tested in cerebrospinal fluid which reduces the applicability of the method, significantly. The main purpose of this paper is to describe research surveys in effects of blood-based biomarkers and diagnostic imaging in AD, using data mining techniques.


  1. Kim Henriksenemail address, Sid E. O’Bryant, HaraldHampel, John Q. Trojanowski, Thomas J. Montine, Andreas Jeromin, KajBlennow, Anders Lönneborg, Tony Wyss-Coray, Holly Soares, Chantal Bazenet, Magnus Sjögren, William Hu, Simon Lovestone, Morten A. Karsdal and Michael W. Weiner, The future of blood-based biomarkers for Alzheimer's disease, Elsevier Alzheimer's and Dementia, (2013)
  2. Britschgi M, Rufibach K, Huang SL, Clark CM, Kaye JA, Li G, Peskind ER, Quinn JF, Galasko DR and WyssCoray T, Modeling of pathological traits in Alzheimer's disease based on systemic extracellular signaling proteome, Molecular and Cellular Proteomics, (2011)
  3. Glenn Fung and Jonathan Stoeckel, SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information, Data Mining, Fifth IEEE International Conference, (2005)
  4. Stefan Klöppel, Cynthia M. Stonnington, Carlton Chu1, Bogdan Draganski1, Rachael I. Scahill, Jonathan D. Rohrer, Nick C. Fox, Clifford R. Jack Jr, John Ashburner1 and Richard S.J. Frackowiak1, Automatic classification of MR scans in Alzheimer's disease, Brain,131(3), 681–689 (2008)
  5. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H and Colliot O; Alzheimer's Disease Neuroimaging Initiative, Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database, Neuro image, (2010)
  6. Long S.S. and Holder L.B., Graph based MRI brain scan classification and correlation discovery, IEEE, Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), (2012)
  7. Jake Yue Chen, ChangyuShen and Andrey Y. Sivachenko, Mining Alzheimer disease relevant proteins from integrated protein interactome data, Pacific Symposium on Biocomputing, 367-378, World Scientific, (2006)