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dc.contributor.authorFulton, Lawrence V.
dc.date.accessioned2019-07-21T21:15:26Z
dc.date.available2019-07-21T21:15:26Z
dc.date.issued2018-02-23
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/8359
dc.descriptionPoster presentation for the Texas State University Translational Health Research 2018 Health Scholar Showcase.
dc.description.abstractPurpose: 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.
dc.description.sponsorshipOffice of Research and Sponsored Programsen_US
dc.formatImage
dc.format.extent1 page
dc.format.medium1 file (.pdf)
dc.language.isoen_USen_US
dc.sourceTexas State University Health Scholar Showcase, 2018, San Marcos, Texas, United States
dc.source.urihttps://www.txstate.edu/research/health/archive/health-scholar-showcase-2018.html
dc.subjectMachine learning
dc.subjectAlzheimer's disease
dc.titleMachine Learning Approaches for Identification of Alzheimer's Disease using Social Determinants & Imageryen_US
txstate.documenttypePoster
txstate.departmentHealth Administration


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