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Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50

1
Department of Health Administration, Texas State University, 601 University Drive, San Marcos, TX 78666, USA
2
Acushnet Holdings Corporation, Acushnet, MA 02743, USA
3
United States Air Force Experimental Test Pilot School, Edwards Air Force Base, CA 93524, USA
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(9), 212; https://doi.org/10.3390/brainsci9090212
Received: 25 July 2019 / Revised: 19 August 2019 / Accepted: 20 August 2019 / Published: 22 August 2019
(This article belongs to the Section Neurotechnology and Neuroimaging)
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. View Full-Text
Keywords: Alzheimer’s disease; extreme gradient boosting; deep residual learning; convolutional neural networks; machine learning; dementia Alzheimer’s disease; extreme gradient boosting; deep residual learning; convolutional neural networks; machine learning; dementia
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MDPI and ACS Style

Fulton, L.V.; Dolezel, D.; Harrop, J.; Yan, Y.; Fulton, C.P. Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50. Brain Sci. 2019, 9, 212. https://doi.org/10.3390/brainsci9090212

AMA Style

Fulton LV, Dolezel D, Harrop J, Yan Y, Fulton CP. Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50. Brain Sciences. 2019; 9(9):212. https://doi.org/10.3390/brainsci9090212

Chicago/Turabian Style

Fulton, Lawrence V., Diane Dolezel, Jordan Harrop, Yan Yan, and Christopher P. Fulton. 2019. "Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50" Brain Sciences 9, no. 9: 212. https://doi.org/10.3390/brainsci9090212

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