Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis

Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis
Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis

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.

Ertek, G., Tokdil, B., Günaydın, İ. “Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis”. In Proceedings of Industrial Conference on Data Mining (ICDM 2014), Springer. Ed: Petra Perner (2014)

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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