@misc{10481/63052,
year = {2020},
url = {http://hdl.handle.net/10481/63052},
abstract = {The detection of Alzheimer’s Disease in its early stages is crucial for patient care and drugs
development. Motivated by this fact, the neuroimaging community has extensively applied machine learning
techniques to the early diagnosis problem with promising results. The organization of challenges has helped
the community to address different raised problems and to standardize the approaches to the problem. In
this work we use the data from international challenge for automated prediction of MCI from MRI data
to address the multiclass classification problem. We propose a novel multiclass classification approach that
addresses the outlier detection problem, uses pairwise t-test feature selection, project the selected features
onto a Partial-Least-Squares multiclass subspace, and applies one-versus-one error correction output
codes classification. The proposed method yields to an accuracy of 67 % in the multiclass classification,
outperforming all the proposals of the competition.},
organization = {Ministerio de Innovacion y Ciencia Project DEEP-NEUROMAPS 	
RTI2018-098913-B100},
organization = {Consejeria de Economia, Innovacion, Ciencia, y Empleo of the Junta de Andalucia 	
A-TIC-080-UGR18 TIC FRONTERA},
organization = {German Research Foundation (DFG)

FPU 18/04902},
organization = {United States Department of Health & Human Services

National Institutes of Health (NIH) - USA

NIH National Institute of Neurological Disorders & Stroke (NINDS)

U01 AG024904},
organization = {DOD ADNI Department of Defense 	
W81XWH-12-2-0012},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {Alzheimer’s disease},
keywords = {CAD},
keywords = {Error correcting output codes},
keywords = {Mild cognitive impairment},
keywords = {Multiclass classification},
keywords = {One versus One},
keywords = {Partial Least Squares},
keywords = {Random Forest},
keywords = {Support vector machines},
title = {Optimized One vs One approach in multiclass classification for early Alzheimer’s Disease and Mild Cognitive Impairment diagnosis},
doi = {10.1109/ACCESS.2020.2997736},
author = {Jiménez Mesa, Carmen and Illan, Ignacio A. and Martín Martín, Alberto and Castillo Barnes, Diego and Martínez Murcia, Francisco Jesús and Gorriz Sáez, Juan Manuel},
}