Classifiers to Predict Conversion from Mild Cognitive Impairment to Dementia by Using Clinical Information and Indices Obtained from Brain Imaging

我们通过使用从脑成像获得的临床信息和指数生成分类器来预测从轻度认知障碍到痴呆的转换。他们预测它是否转化，以及何时转化。我们设计的线性和非线性分类器的基础上监督学习方法。增量诊断价值的参数从成像和统计参数映射得到澄清，通过比较性能的分类器，并没有将图像相关的输入。

We generated classifiers to predict the conversion from mild cognitive impairment to dementia by using clinical information and indices obtained from brain imaging. They predict whether it converts or not, and when converts. We contrived linear and non-linear classifiers on the basis of supervised learning methodology. Incremental diagnostic value of parameters obtained from imaging and statistical parametric mapping was clarified by comparing performances of classifiers with and without incorporating image-related inputs.