Machine Learning Approaches for Identification of Alzheimer's Disease using Social Determinants and Imagery
|dc.contributor.author||Fulton, Lawrence V. ( 0000-0001-8603-1913 )|
|dc.identifier.citation||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, TX.|
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.
|dc.description.sponsorship||Office of Research and Sponsored Programs||en_US|
|dc.format.medium||1 file (.pdf)|
|dc.source||Texas State University Health Scholar Showcase, 2018, San Marcos, Texas, United States|
|dc.title||Machine Learning Approaches for Identification of Alzheimer's Disease using Social Determinants and Imagery||en_US|