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Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a defi...

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Autores principales: Battineni, Gopi, Hossain, Mohmmad Amran, Chintalapudi, Nalini, Traini, Enea, Dhulipalla, Venkata Rao, Ramasamy, Mariappan, Amenta, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623867/
https://www.ncbi.nlm.nih.gov/pubmed/34829450
http://dx.doi.org/10.3390/diagnostics11112103
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author Battineni, Gopi
Hossain, Mohmmad Amran
Chintalapudi, Nalini
Traini, Enea
Dhulipalla, Venkata Rao
Ramasamy, Mariappan
Amenta, Francesco
author_facet Battineni, Gopi
Hossain, Mohmmad Amran
Chintalapudi, Nalini
Traini, Enea
Dhulipalla, Venkata Rao
Ramasamy, Mariappan
Amenta, Francesco
author_sort Battineni, Gopi
collection PubMed
description Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.
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spelling pubmed-86238672021-11-27 Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms Battineni, Gopi Hossain, Mohmmad Amran Chintalapudi, Nalini Traini, Enea Dhulipalla, Venkata Rao Ramasamy, Mariappan Amenta, Francesco Diagnostics (Basel) Article Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy. MDPI 2021-11-13 /pmc/articles/PMC8623867/ /pubmed/34829450 http://dx.doi.org/10.3390/diagnostics11112103 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
Battineni, Gopi
Hossain, Mohmmad Amran
Chintalapudi, Nalini
Traini, Enea
Dhulipalla, Venkata Rao
Ramasamy, Mariappan
Amenta, Francesco
Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_full Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_fullStr Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_full_unstemmed Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_short Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_sort improved alzheimer’s disease detection by mri using multimodal machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623867/
https://www.ncbi.nlm.nih.gov/pubmed/34829450
http://dx.doi.org/10.3390/diagnostics11112103
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