Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 30 July 2019 / Approved: 31 July 2019 / Online: 31 July 2019 (04:33:43 CEST)

How to cite: 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. Preprints 2019, 2019070345 (doi: 10.20944/preprints201907.0345.v1). 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. Preprints 2019, 2019070345 (doi: 10.20944/preprints201907.0345.v1).

Abstract

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 Mini-Mental State Exam (MMSE). A Residual Network with 50 layers (ResNet-50) predicted 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 4,139 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.

Subject Areas

Alzheimer’s Disease; Extreme Gradient Boosting; Deep Residual Learning; conolutional neural networks; machine learning; dementia

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