Adaptive LASSO logistic regression based on particle swarm optimization for Alzheimer's disease early diagnosis

Accurate classification of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI) play key roles in computer-assisted intervention for the diagnosis of AD. However, not all features of AD data will lead to a good classification result, because there are always some unrelated and redundant features. To solve this problem, an adaptive LASSO logistic regression model based on particle swarm optimization(PSO-ALLR)is proposed. This algorithm consists of two stages. In the first stage, the particle swarm optimization (PSO) algorithm is used for global search to remove redundant features and reduces the computational time for the later stage. In the second stage, the adaptive LASSO serves as a local search to select the most relevant features for AD classification.We evaluate the performance of the proposed method on 197 subjects from the baseline MRI data of ADNI database. The proposed method achieves a classification accuracy of 96.27%, 84.81%, and 76.13%, for AD vs. HC, MCI vs. HC, and cMCI vs. sMCI, respectively.