Cargando…

Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines

Alzheimer’s disease (AD) is a neurodegenerative disease that mainly affects older adults. Currently, AD is associated with certain hypometabolic biomarkers, beta-amyloid peptides, hyperphosphorylated tau protein, and changes in brain morphology. Accurate diagnosis of AD, as well as mild cognitive im...

Descripción completa

Detalles Bibliográficos
Autores principales: Sánchez-Reyna, Ana G., Celaya-Padilla, José M., Galván-Tejada, Carlos E., Luna-García, Huizilopoztli, Gamboa-Rosales, Hamurabi, Ramirez-Morales, Andres, Galván-Tejada, Jorge I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391811/
https://www.ncbi.nlm.nih.gov/pubmed/34442108
http://dx.doi.org/10.3390/healthcare9080971
_version_ 1783743359699910656
author Sánchez-Reyna, Ana G.
Celaya-Padilla, José M.
Galván-Tejada, Carlos E.
Luna-García, Huizilopoztli
Gamboa-Rosales, Hamurabi
Ramirez-Morales, Andres
Galván-Tejada, Jorge I.
author_facet Sánchez-Reyna, Ana G.
Celaya-Padilla, José M.
Galván-Tejada, Carlos E.
Luna-García, Huizilopoztli
Gamboa-Rosales, Hamurabi
Ramirez-Morales, Andres
Galván-Tejada, Jorge I.
author_sort Sánchez-Reyna, Ana G.
collection PubMed
description Alzheimer’s disease (AD) is a neurodegenerative disease that mainly affects older adults. Currently, AD is associated with certain hypometabolic biomarkers, beta-amyloid peptides, hyperphosphorylated tau protein, and changes in brain morphology. Accurate diagnosis of AD, as well as mild cognitive impairment (MCI) (prodromal stage of AD), is essential for early care of the disease. As a result, machine learning techniques have been used in recent years for the diagnosis of AD. In this research, we propose a novel methodology to generate a multivariate model that combines different types of features for the detection of AD. In order to obtain a robust biomarker, ADNI baseline data, clinical and neuropsychological assessments (1024 features) of 106 patients were used. The data were normalized, and a genetic algorithm was implemented for the selection of the most significant features. Subsequently, for the development and validation of the multivariate classification model, a support vector machine model was created, and a five-fold cross-validation with an AUC of 87.63% was used to measure model performance. Lastly, an independent blind test of our final model, using 20 patients not considered during the model construction, yielded an AUC of 100%.
format Online
Article
Text
id pubmed-8391811
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-83918112021-08-28 Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines Sánchez-Reyna, Ana G. Celaya-Padilla, José M. Galván-Tejada, Carlos E. Luna-García, Huizilopoztli Gamboa-Rosales, Hamurabi Ramirez-Morales, Andres Galván-Tejada, Jorge I. Healthcare (Basel) Article Alzheimer’s disease (AD) is a neurodegenerative disease that mainly affects older adults. Currently, AD is associated with certain hypometabolic biomarkers, beta-amyloid peptides, hyperphosphorylated tau protein, and changes in brain morphology. Accurate diagnosis of AD, as well as mild cognitive impairment (MCI) (prodromal stage of AD), is essential for early care of the disease. As a result, machine learning techniques have been used in recent years for the diagnosis of AD. In this research, we propose a novel methodology to generate a multivariate model that combines different types of features for the detection of AD. In order to obtain a robust biomarker, ADNI baseline data, clinical and neuropsychological assessments (1024 features) of 106 patients were used. The data were normalized, and a genetic algorithm was implemented for the selection of the most significant features. Subsequently, for the development and validation of the multivariate classification model, a support vector machine model was created, and a five-fold cross-validation with an AUC of 87.63% was used to measure model performance. Lastly, an independent blind test of our final model, using 20 patients not considered during the model construction, yielded an AUC of 100%. MDPI 2021-07-31 /pmc/articles/PMC8391811/ /pubmed/34442108 http://dx.doi.org/10.3390/healthcare9080971 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sánchez-Reyna, Ana G.
Celaya-Padilla, José M.
Galván-Tejada, Carlos E.
Luna-García, Huizilopoztli
Gamboa-Rosales, Hamurabi
Ramirez-Morales, Andres
Galván-Tejada, Jorge I.
Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines
title Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines
title_full Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines
title_fullStr Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines
title_full_unstemmed Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines
title_short Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines
title_sort multimodal early alzheimer’s detection, a genetic algorithm approach with support vector machines
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391811/
https://www.ncbi.nlm.nih.gov/pubmed/34442108
http://dx.doi.org/10.3390/healthcare9080971
work_keys_str_mv AT sanchezreynaanag multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines
AT celayapadillajosem multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines
AT galvantejadacarlose multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines
AT lunagarciahuizilopoztli multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines
AT gamboarosaleshamurabi multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines
AT ramirezmoralesandres multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines
AT galvantejadajorgei multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines
AT multimodalearlyalzheimersdetectionageneticalgorithmapproachwithsupportvectormachines