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

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 tech- niques from machine learning and data mining. The applied methods include classifica- tion 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 in- sights obtained in this study can be used by individuals and health professionals to as- sess possible risks, and take preventive measures.