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Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50
Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early dete...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770938/ https://www.ncbi.nlm.nih.gov/pubmed/31443556 http://dx.doi.org/10.3390/brainsci9090212 |
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author | Fulton, Lawrence V. Dolezel, Diane Harrop, Jordan Yan, Yan Fulton, Christopher P. |
author_facet | Fulton, Lawrence V. Dolezel, Diane Harrop, Jordan Yan, Yan Fulton, Christopher P. |
author_sort | Fulton, Lawrence V. |
collection | PubMed |
description | Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review. |
format | Online Article Text |
id | pubmed-6770938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67709382019-10-30 Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50 Fulton, Lawrence V. Dolezel, Diane Harrop, Jordan Yan, Yan Fulton, Christopher P. Brain Sci Article Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review. MDPI 2019-08-22 /pmc/articles/PMC6770938/ /pubmed/31443556 http://dx.doi.org/10.3390/brainsci9090212 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fulton, Lawrence V. Dolezel, Diane Harrop, Jordan Yan, Yan Fulton, Christopher P. Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50 |
title | Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50 |
title_full | Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50 |
title_fullStr | Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50 |
title_full_unstemmed | Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50 |
title_short | Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50 |
title_sort | classification of alzheimer’s disease with and without imagery using gradient boosted machines and resnet-50 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770938/ https://www.ncbi.nlm.nih.gov/pubmed/31443556 http://dx.doi.org/10.3390/brainsci9090212 |
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