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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease
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A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease

机译：基于高斯判别分析的生成学习算法，用于阿尔茨海默氏病轻度认知障碍的早期诊断

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Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains unsatisfactory, especially for delineating the MCI group from the cognitively normal control (CN) group. This study introduces a novel algorithm based on the Gaussian discriminant analysis (GDA) for a more effective and accurate classification performance. Subjects considered in this study included 190 CN, 305 MCI, and 133 AD subjects. Using 75% of the data as the training set with a tenfold cross validation, the proposed algorithm achieved an average accuracy of 94.17%, a sensitivity of 93.00%, and a specificity of 95.00% for discriminating AD from CN; and an average accuracy of 84.86%, a sensitivity of 84.78%, and a specificity of 85.00% for discriminating MCI from CN. Then a true test was implemented for the remaining 25% data, for discriminating specifically MCI from CN, resulting in an accuracy of 82.20%, a sensitivity of 83.10%, and a specificity of 80.85%. As revealed through the literature, these results involving the delineation of the MCI group from CN could be considered as the best classification performance obtained so far. This study also shows that by separating left and right hemispheres of the brain into two decision spaces, then combining the results of these two spaces, the classification performance can be improved significantly; an assertion proven in this study.
机译：在过去的几年中，已提出了几种方法来协助阿尔茨海默氏病（AD）的早期诊断及其轻度认知障碍（MCI）的前驱阶段。为了解决这个高维分类问题，广泛使用的算法仍然是支持向量机（SVM）。但是由于数据的高差异性，SVM的分类性能仍然不能令人满意，特别是对于将MCI组与认知正常对照组（CN）进行区分的情况。这项研究介绍了一种基于高斯判别分析（GDA）的新颖算法，可实现更有效和准确的分类性能。本研究中考虑的受试者包括190名CN，305名MCI和133名AD受试者。使用10％交叉验证的75％数据作为训练集，该算法实现了将AD与CN区分的平均准确度为94.17％，灵敏度为93.00％和特异度为95.00％。从CN区分MCI的平均准确度为84.86％，灵敏度为84.78％和特异度为85.00％。然后对剩余的25％的数据进行了真实测试，以从CN中特异性地区分MCI，从而获得了82.20％的准确度，83.10％的灵敏度和80.85％的特异性。从文献中可以看出，这些涉及从CN划定MCI组的结果可以认为是迄今为止获得的最佳分类性能。这项研究还表明，通过将大脑的左半球和右半球分成两个决策空间，然后将这两个空间的结果相结合，可以显着提高分类性能;在这项研究中证明的断言。

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