Feature selection and transfer learning for Alzheimer's Disease clinical diagnosis

Abstract

Background and Purpose: A majority studies on diagnosis of Alzheimer's Disease (AD) are based on an assumption: the training and testing data are drawn from the same distribution. However, in the diagnosis of AD and mild cognitive impairment (MCI), this identical-distribution assumption may not hold. To solve this problem, we utilize the transfer learning method into the diagnosis of AD. Methods: The MR (Magnetic Resonance) images were segmented using spm-Dartel toolbox and registrated with Automatic Anatomical Labeling (AAL) atlas, then the gray matter (GM) tissue volume of the anatomical region were computed as characteristic parameter. The information gain was introduced for feature selection. The TrAdaboost algorithm was used to classify AD, MCI, and normal controls (NC) data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, meanwhile, the "knowledge" learned from ADNI was transferred to AD samples from local hospital. The classification accuracy, sensitivity and specificity were calculated and compared with four classical algorithms. Results: In the experiment of transfer task: AD to MCI, 177 AD and 40NC subjects were grouped as training data; 245 MCI and 45 remaining NC subjects were combined as testing data, the highest accuracy achieved 85.4%, higher than the other four classical algorithms. Meanwhile, feature selection that is based on information gain reduced the features from 90 to 7, controlled the redundancy efficiently. In the experiment of transfer task: ADNI to local hospital data, the highest accuracy achieved 93.7%, and the specificity achieved 100%. Conclusions: The experimental results showed that our algorithm has a clear advantage over classic classification methods with higher accuracy and less fluctuation.