A feature selection method for classification of ADHD

Bo Miao, Yulin Zhang

Research output: Chapter in Book/Conference paperConference paperpeer-review

20 Citations (Scopus)

Abstract

At present, the classification of brain diseases through neuroimaging data is a hot topic. Attention deficit hyperactivity disorder (ADHD) is usually diagnosed by the standard scale. However, the traditional diagnostic methods have high misdiagnosis rate and time consuming. In this paper, we discussed the classification of ADHD by using the feature subset obtained by preprocessing and feature selection of fractional amplitude of low-frequency fluctuation (fALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) data. We proposed a feature selection algorithm based on Relief algorithm and verification accuracy (VA-Relief). The experimental results show that fALFF can be used to realize the high accuracy classification of ADHD by using our feature selection algorithm and preprocessing method. Therefore, it is possible to use rs-fMRI data and machine learning methods to assist the diagnosis of brain diseases.

Original languageEnglish
Title of host publicationICCSS 2017 - 2017 International Conference on Information, Cybernetics, and Computational Social Systems
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages21-25
Number of pages5
ISBN (Electronic)9781538632574
DOIs
Publication statusPublished - 31 Oct 2017
Externally publishedYes
Event2017 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2017 - Dalian, Liaoning, China
Duration: 24 Jul 201726 Jul 2017

Publication series

NameICCSS 2017 - 2017 International Conference on Information, Cybernetics, and Computational Social Systems

Conference

Conference2017 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2017
Country/TerritoryChina
CityDalian, Liaoning
Period24/07/1726/07/17

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