The Use of Decision Trees to Detect Alzheimer's Disease

Alzheimer's Disease is the most common disease of dementia, which may involve the decline of memory, communication, and judgement, yet it is hard to diagnose. The machine learning algorithms, Decision Trees, provide a possible solution to reduce the difficulty of the diagnosis process of Alzheimer's Disease. This thesis studied the use of Decision Trees to detect Alzheimer's Disease by investigating 27 different Decision Tree models derived from applying nine datasets, concerning Alzheimer's Disease, on three Decision Tree algorithms. The datasets utilized were the OASIS: Cross-Sectional, OASIS: Longitudinal, and OASIS-3 datasets, each of which had three variants; Unmoderated that contained missing values, No Missing Values that contained no missing values, and Reduced Attributes that contained no missing values and no attributes of low importance. The algorithms utilized were the C4.5, CART, and CHAID Decision Tree algorithms. The results showed that the C4.5 algorithm and the OASIS-2 datasets performed worse than their alternatives. Moreover, it showed that the CART algorithm, the Unmoderated OASIS-1 dataset, and the No Missing Values and Reduced Attributes OASIS-3 datasets performed better than their alternatives. The results also reveal that the worst Decision Tree model obtained in the experiment had a prediction accuracy of 76.94%, 55 number of nodes, and a depth 10. In contrast, the best model had a prediction accuracy of 90.37%, 5 number of nodes, and a depth 2. The results suggest that Decision Trees are suitable models to detect Alzheimer's Disease