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Optimized One vs One Approach in Multiclass Classification for Early Alzheimer’s Disease and Mild Cognitive Impairment Diagnosis

IEEE Access(2020)10.1109/access.2020.2997736Source: DataRank Database

Optimized One vs One Approach in Multiclass Classification for Early Alzheimer’s Disease and Mild Cognitive Impairment Diagnosis is a research paper published in IEEE Access (2020). On theSindex it has a DataRank of 0. It has been cited 41 times.

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Open Access41 citations

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Enrichment:Funding (7 grants)OA: goldTopics (13)SDGs (1)

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Authors (7)

I. Álvarez ORCID,Alberto Martín‐Martín ORCID,Diego Castillo-Barnés ORCID,Francisco J. Martínez-Murcia ORCID,Javier Ramı́rez ORCID

Live enrichment

Ministerio de Innovación y Ciencia Project DEEP-NEUROMAPS

Grant: RTI2018-098913-B100

Consejería de Economía, Innovación, Ciencia, y Empleo of the Junta de Andalucía

Grant: A-TIC-080-UGR18 TIC FRONTERA

Ministerio de Universidades under the FPU Predoctoral

Grant: FPU 18/04902

Alzheimer’s Disease Neuroimaging Initiative (ADNI), National Institutes of Health

Grant: U01 AG024904

DOD ADNI Department of Defense

Grant: W81XWH-12-2-0012

National Institutes of Health

Grant: 1U01AG024904-01

Alzheimers Disease Neuroimaging Initiative

Canadian Institutes of Health Research

Grant: unidentified

unidentified

goldLicense: CC BY

publisherhttp://xplorestaging.ieee.org/ielx7/6287639/8948470/09099858.pdf?arnumber=9099858

https://doi.org/10.1109/access.2020.2997736

https://ieeexplore.ieee.org/ielx7/6287639/8948470/09099858.pdf

http://hdl.handle.net/10481/63052

https://dx.doi.org/10.1109/ACCESS.2020.2997736

Fields of Study

02 engineering and technology03 medical and health sciences0302 clinical medicine0202 electrical engineering, electronic engineering, information engineering

Keywords

Random ForestSupport vector machinesOne versus OneMild cognitive impairmentPartial Least SquaresMulticlass classificationError correcting output codesCADAlzheimer’s disease

Sustainable Development Goals

SDG 3: 3. Good health