Alzheimer's Disease Classification using LightGBM and Euclidean Distance Map

Alzheimer's Disease (AD) classification is very helpful for timely treatment and intervention in its early stage so as to reduce the incidence rate of AD and delay AD progression in elderly population. In this paper, a feature extraction method utilizing Euclidean distance map was proposed. Then the features for AD classification were selected in accordance with the feature importance score calculated by LightGBM (LGB). At last, the LGB classifier was applied to classifying AD patient utilizing selected features. The proposed AD classification method was evaluated on real dataset. The experimental results show that the proposed feature extraction and selection methods are effective, and the LGB classifier using selected features is superior to other conventional classifiers.