AUTHOR=Meng Xianglian , Wu Yue , Liu Wenjie , Wang Ying , Xu Zhe , Jiao Zhuqing 

TITLE=Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.856295

DOI=10.3389/fninf.2022.856295

ISSN=1662-5196

ABSTRACT=Alzheimer's disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for Alzheimer's disease. Specifically, using 649 voxel-based morphometry (VBM) obtained from magnetic resonance imaging in ADNI, we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-level, gene-level and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (Healthy Control), LMCI (Late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, Least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM and RS-SVM five methods to test and compare the accuracy of these features in three groups. The AD-HC group had more than 90% prediction accuracy, while the other four methods all peaked below 90%. Additionally, we performed functional analysis of the features to explain the biological significance. The experimental results using 5 machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy and our strategy can identify important brain regions for AD.