TY - GEN
T1 - Risk factors and identifiers for Alzheimer's disease
T2 - 14th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2014
AU - Ertek, Gürdal
AU - Tokdil, Bengi
AU - GünaydIn, Ibrahim
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84958536110&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958536110&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08976-8_1
DO - 10.1007/978-3-319-08976-8_1
M3 - Conference contribution
AN - SCOPUS:84958536110
SN - 9783319089751
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 11
BT - Advances in Data Mining
PB - Springer Verlag
Y2 - 16 July 2014 through 20 July 2014
ER -