Comparison and Analyzation of Different Feature Parameters for Alzheimer's disease Identification

In this paper, we compare the performance of the derived anatomical features and the extracted feature parameters in Alzheimer's disease (AD) identification. The correlation relationship between them and clinical mini-mental state examination (MMSE) score is analyzed. Based on these feature parameters, the highly correlated combined feature vectors are built and used as variables for the presented modified elastic net (EN) classifier. Experimental results show that the extracted feature parameters can obtain similar identification performance with the cortical thickness and the volume of gray matter (GM) in AD identification. The highly correlated combined feature vectors show the best identification performance among all of feature parameters using the modified EN-based classifier.