Classification of ADHD with bi-objective optimization.
Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data with classification techniques to aid the diagnosis of ADHD and propose a bi-objective ADHD classification scheme based on L1-norm support vector machi...
| Publicado en: | Journal of Biomedical Informatics Vol. 84; pp. 164 - 171 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130990534&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130990534 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Aug2018 vid: 84 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 130990534 130990534 NLM30009990 130990534 10.1016/j.jbi.2018.07.011 NLM30009990 130990534 ppf: 164 ppct: 7 formats: tig: atl: Classification of ADHD with bi-objective optimization. aug: au: Shao, Lizhen Xu, Yadong Fu, Dongmei affil: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China sug: subj: Attention Deficit Hyperactivity Disorder Medical Informatics Methods Magnetic Resonance Imaging Decision Making Attention Deficit Hyperactivity Disorder Classification Image Processing, Computer Assisted Algorithms Reproducibility of Results Brain Mapping Child Models, Statistical Human Resource Databases Validation Studies Comparative Studies Evaluation Research Multicenter Studies Barthel Index Dyadic Adjustment Scale Child: 6-12 years ab: Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data with classification techniques to aid the diagnosis of ADHD and propose a bi-objective ADHD classification scheme based on L1-norm support vector machine (SVM). In our classification model, two objectives, namely, the margin of separation and the empirical error are considered at the same time. Then the normal boundary intersection (NBI) method of Das and Dennis is used to solve the bi-objective optimization problem. A representative nondominated set which reflects the entire trade-off information between the two objectives is obtained. Each representative nondominated point in the set corresponds to an efficient classifier. Finally a decision maker can choose a final efficient classifier from the set according to the performance of each classifier. Our scheme avoids the trial and error process for regularization hyper-parameter selection. Experimental results show that our bi-objective optimization classification scheme for ADHD diagnosis performs considerably better than some traditional classification methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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