Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment

Antonio Martínez-Torteya, Víctor Treviño, José G. Tamez-Peña

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers. For this, Alzheimer's Disease Neuroimaging Initiative database was mined for case-control candidates. At least 652 baseline features extracted from MRI and PET analyses, biological samples, and clinical data up to February 2014 were used. A feature selection methodology that includes a genetic algorithm search coupled to a logistic regression classifier and forward and backward selection strategies was used to explore combinations of features. This generated diagnostic models with sizes ranging from 3 to 8, including well documented AD biomarkers, as well as unexplored image, biochemical, and clinical features. Accuracies of 0.85, 0.79, and 0.80 were achieved for HC-AD, HC-MCI, and MCI-AD classifications, respectively, when evaluated using a blind test set. In conclusion, a set of features provided additional and independent information to well-established AD biomarkers, aiding in the classification of MCI and AD.

Original languageEnglish
Article number961314
Pages (from-to)961314
JournalBioMed Research International
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 Antonio Martínez-Torteya et al.

Copyright 2015 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology


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