Feature Selection and Compression of rs-fMRI Functional ConnectioninMCI
The diagnosis and timely treatment of mild cognitive impairment(MCI)has clinical significance in provi-ding early warning signals for Alzheimer's Disease.The performance of the methods for auxiliary diagnosis of MCI by neuroimaging technology and machine learning(ML)is mainly depend on how to screenfor features that can ex-press significant differences between groups.The commonly used Pearson correlation method represents the brain functional connection(FC)and take it as the input features of the classifier.Usually,A large number of features that contain redundant information can create a dimension curse problem.Thus,proposing a method combining feature selection and feature compression to get important features to solve the problem.Firstly,Sliding window technology is used to perform dynamic functional connectivity(DFC).Secondly,the minimum in-class distance criterion is used to get important features.Then the selected features are compressed by least square(LS).Finally,the fitting coeffi-cient obtained by LS is used as the latest feature to achieve classification.The experimental results show that the classification accuracy of the algorithm combining feature compression and feature selection can achieve 76%,which is about 8%higher than the traditional method.It can effectively improve the classification accuracy of MCI,which has certain biological significance.
machine learningresting-state functional magnetic resonance imagingdynamic functional connectivi-tyfeature selectionfeature compression
万方数据
维普
