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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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 |
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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 |
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