Abstract

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Title:: Application of KPCA and Adaboost algorithm in the classification of functional magnetic resonance images of Alzheimer’s disease

Author(s):: LI Changsheng; WANG Yu; XIAO Hongbing; XING Suxia; Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Keywords: functional magnetic resonance imaging; Alzheimer’s disease; mild cognitive impairment; functional connection matrix; kernel principal component analysis

Abstract:: Abstract: The purpose of this study is to achieve the auxiliary diagnosis and analysis of Alzheimer’s disease (AD) by analyzing and characterizing brain functional magnetic resonance imaging (fMRI) data using machine learning method. After the fMRI data is preprocessed and the covariate is removed, the brain of each subject is divided into 116 brain regions according to anatomical automatic labeling template, and the whole brain functional connection matrix is constructed by extracting the time series of each brain region. Kernel principal component analysis is used to extract features and Adaboost algorithm is used for classification. The results of the experiment on fMRI images of 34 patients with AD, 35 patients with mild cognitive impairments and 35 normal controls show that using resting state fMRI combined with machine learning method can effectively realize the accurate classification of AD, with a classification accuracy rate up to 96%. The proposed method can provide an effective basis for the auxiliary diagnosis of patients with AD.