Risk factors and identifiers for Alzheimer's disease: A data mining analysis

Gürdal Ertek, Bengi Tokdil, Ibrahim GünaydIn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Data Mining
Subtitle of host publicationApplications and Theoretical Aspects - 14th Industrial Conference, ICDM 2014, Proceedings
PublisherSpringer Verlag
Pages1-11
Number of pages11
ISBN (Print)9783319089751
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event14th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2014 - St. Petersburg, Russian Federation
Duration: Jul 16 2014Jul 20 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8557 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2014
Country/TerritoryRussian Federation
CitySt. Petersburg
Period7/16/147/20/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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