Abstract
The topic of this paper is the Alzheimer’s Disease (AD), with the goal being the analysis of risk factors and identifying tests that can help diagnose AD. While there exists multiple studies that analyze the factors that can help diagnose or predict AD, this is the first study that considers only non-image data, while using a multitude of techniques from machine learning and data mining. The applied methods include classification tree analysis, cluster analysis, data visualization, and classification analysis. All the analysis, except classification analysis, resulted in insights that eventually lead to the construction of a risk table for AD. The study contributes to the literature not only with new insights, but also by demonstrating a framework for analysis of such data. The insights obtained in this study can be used by individuals and health professionals to assess possible risks, and take preventive measures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alzheimer Canada (2013), http://www.alzheimer.ca/en/Get-involved/Raise-your-voice/WHO-report-dementia-2012 (accessed on January 24, 2013)
Alzheimer.Org, http://www.alz.org/dementia/types-of-dementia.asp (accessed on January 24, 2013)
Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience 22, 2677–2684 (2010)
Supplement for “Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis”, http://people.sabanciuniv.edu/ertekg/papers/supp/11.pdf
Unay, D., Chen, X., Erçil, A., Çetin, M., Jasinschi, R., van Buchem, M.A., Ekin, A.: Binary and nonbinary description of hypointensity for search and retrieval of brain MR images. In: IS&T/SPIE Electronic Imaging, Multimedia Content Access: Algorithms and Systems III, San Jose, California, USA (January 2009)
WHO, Dementia: a public health priority. World Health Organization and Alzheimer’s Disease International (2012), http://www.who.int/mental_health/publications/dementia_report_2012/en/ (accessed on November 13, 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ertek, G., Tokdil, B., Günaydın, İ. (2014). Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-08976-8_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08975-1
Online ISBN: 978-3-319-08976-8
eBook Packages: Computer ScienceComputer Science (R0)