Machine Learning Approaches for Identification of Alzheimer's Disease using Social Determinants and Imagery




Fulton, Lawrence V.

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Purpose: The purpose of this study is to predict the presence of Alzheimer's Disease (AD) using socio-demographic, clinical, and Magnetic Resonance Imaging (MRI) 4D data. Significance: Early detection of AD enables family planning and may reduce costs by delaying long-term care (Alzheimer's Association, 2018). Accurate, non-imagery methods also reduce patient costs. Methods: Extreme Gradient Boosted random forests (XGBoost) predict Clinical Dementia Rating (CDR) presence and severity as a function of gender, age, education, socioeconomic status (SES), and Mini-Mental Status Exam (MMSE). Convulutional Neural Networks (CNN) predict CDR from MRI's transformed to Eigenbrain imagery. XGBoost also predicts CDR with additional clinical variables. Results: XGBoost provides 93% prediction accuracy for CDR using socio-demographic and clinical non-imagery variables-92% accuracy when clinical measures are excluded. CNN using the transformed Eigenbrain imagery results in 93% prediction accuracy. Conclusion: ML methods predict AD with high accuracy. Non-imagery analysis may be nearly as efficacious as imagery prediction at a fraction of the cost.



machine learning, Alzheimer's disease, Health Administration


Fulton, L. V. (2018). Machine learning approaches for identification of Alzheimer's disease using social determinants and imagery. Poster presented at the Texas State University Health Scholar Showcase, San Marcos, Texas.


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