Improved classification of Alzheimer's disease data via removal of nuisance variability.
Improved classification of Alzheimer's disease data via removal of nuisance variability.
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DOI：
10.1371/journal.pone.0031112
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Koikkalainen J;Pölönen H;Mattila J;van Gils M;Soininen H;Lötjönen J;Alzheimer's Disease Neuroimaging Initiative
阿尔茨海默病的诊断基于神经心理学测试的结果和可用的支持生物标志物，例如影像学研究的结果。测试结果和生物标志物的值取决于令人讨厌的特征，例如年龄和性别。为了提高诊断能力，必须从数据中消除干扰特征的影响。在本文中，识别了分类特征和滋扰特征之间的四种类型的相互作用。测试了三种方法来从分类数据中消除这些相互作用。在分层分析中，从训练集中生成同质子组。数据校正方法利用线性回归模型来消除数据中的干扰特征的影响。第三种方法是这两种方法的结合。这些方法使用来自阿尔茨海默病神经影像计划数据库的所有基线数据在两项分类研究中进行了测试：对阿尔茨海默病患者中的对照受试者进行分类，并区分稳定和进行性轻度认知障碍受试者。结果表明，分层分析和数据校正都能够在统计上显着提高几种神经心理学测试和影像生物标志物的分类准确性。对于稳定和进行性轻度认知障碍受试者的分类，改善尤其大，观察到的最佳改善为 6% 单位。数据校正方法为成像生物标志物提供了更好的结果，而分层分析在神经心理学测试中效果很好。总之，研究表明，应从数据中消除由干扰特征引起的过度变异，以提高分类准确性，从而提高诊断的可靠性。
Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making.
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DOI：
10.1523/jneurosci.3252-09.2009
发表时间：
2009-12-02
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Fjell AM;Walhovd KB;Fennema-Notestine C;McEvoy LK;Hagler DJ;Holland D;Brewer JB;Dale AM
通讯作者：
Dale AM
影响因子：
3.7
作者：
Dukart J;Schroeter ML;Mueller K;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
9.9
作者：
SAUNDERS, AM;STRITTMATTER, WJ;ROSES, AD
通讯作者：
ROSES, AD
影响因子：
120.1
作者：
Schilsky, Richard L.
通讯作者：
Schilsky, Richard L.
影响因子：
5
作者：
Chawla, NV;Bowyer, KW;Kegelmeyer, WP
通讯作者：
Kegelmeyer, WP