Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment of AD. However, compared with AD classification tasks, predicting the conversion of MCI to AD is relatively difficult. as there are only minor differences among MCI groups. What’s more, in brain imaging analysis, the high dimensionality and relatively small number of subjects brings challenges to computer-aided diagnosis of AD and MCI. Many previous researches focused on the identification of imaging biomarkers for AD diagnosis. In this paper, we introduce sparse logistic regression for the early diagnosis of AD. Sparse logistic regression (SLR) uses L_1/2 regularization to impose a sparsity constraint on logistic regression. The L_1/2 regularization is considered a representative of Lq regularization, where fewer but informative key brain regions are applied for the classification of AD/MCI. We evaluated the SLR on 197 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that the SLR improves the classification performance of AD/MCI compared other classical methods.

阿尔茨海默氏病（AD）及其前驱阶段，即轻度认知障碍（MCI）的准确分类对于有效治疗AD至关重要。但是，与AD分类任务相比，预测MCI到AD的转换相对困难。因为MCI组之间只有很小的差异。此外，在脑成像分析中，高维数和相对较少的对象数量给AD和MCI的计算机辅助诊断带来了挑战。先前的许多研究都集中在用于AD诊断的成像生物标志物的鉴定上。在本文中，我们介绍了稀疏逻辑回归用于AD的早期诊断。稀疏逻辑回归（SLR）使用L _1/2正则化对逻辑回归施加稀疏约束。L _1/2正则化被认为是Lq正则化的代表，其中较少但有用的关键大脑区域被用于AD / MCI的分类。我们从阿尔茨海默氏病神经影像学倡议（ADNI）数据库评估了197位受试者的SLR。实验结果表明，与其他经典方法相比，单反提高了AD / MCI的分类性能。