【摘要】 提出了一种基于L₂正则化逻辑回归（LR）的阿尔茨海默病（AD）诊断算法.在该模型中使用了L₂范数对LR进行正则化处理,正则化参数通过十倍交叉验证来选择,同时使用了独立成分分析对预处理后的数据进行降维处理,最后使用了牛顿算法来求出模型的最优权值.通过这一算法可以有效分辨出AD及其早期阶段轻度认知障碍（MCI）.实验在AD vs.CN,MCI vs.CN和LMCI vs.EMCI 3组分类任务中获得的分类准确率分别为95.22%,81.22%和74.35%.实验结果证明其为一种有效的诊断算法.更多还原

【Abstract】 In this paper, an algorithm for the diagnosis of Alzheimer’s disease（AD） based on L₂ regularized logistic regression（LR） is proposed. Among them, the L₂ norm is used to regularize the LR. The regularization parameters are selected through ten-fold cross-validation. At the same time, independent component analysis is used to reduce the dimensionality of the preprocessed data. Finally, the Newton algorithm is used to find the optimal weight of the model. This algorithm can effectively distinguish AD and its early-stage mild cognitive impairment（MCI）. The classification accuracies obtained by the experiment in the three groups of classification tasks of AD vs. CN,MCI vs. CN and LMCI vs. EMCI were 95.22%,81.22%and74.35%,respectively. The results proved that it is an effective diagnostic algorithm.更多还原