Machine Learning Approaches for Identification of Alzheimer's Disease using Social Determinants and Imagery
| dc.contributor.author | Fulton, Lawrence V. | |
| dc.date.accessioned | 2019-07-21T21:15:26Z | |
| dc.date.available | 2019-07-21T21:15:26Z | |
| dc.date.issued | 2018-02 | |
| dc.description.abstract | 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.department | Health Administration | |
| dc.description.sponsorship | Office of Research and Sponsored Programs | |
| dc.format | Image | |
| dc.format.extent | 1 page | |
| dc.format.medium | 1 file (.pdf) | |
| 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, Texas. | |
| dc.identifier.uri | https://hdl.handle.net/10877/8359 | |
| dc.language.iso | en | |
| dc.source | Texas State University Health Scholar Showcase, 2018, San Marcos, Texas, United States | |
| dc.subject | machine learning | |
| dc.subject | Alzheimer's disease | |
| dc.subject | Health Administration | |
| dc.title | Machine Learning Approaches for Identification of Alzheimer's Disease using Social Determinants and Imagery | |
| dc.type | Poster |