Machine Learning Approaches for Predicting Progression to Alzheimer’s Disease in Patients with Mild Cognitive Impairment
The proposed SHAP for feature selection combined with XGBoost may provide improved predictive accuracy in diagnosing Alzheimer's patients.
Authors
- Christopher G. Kevil (ORCID: https://orcid.org/0000-0003-0863-7260)
- John A. Vanchiere (ORCID: https://orcid.org/0000-0001-6863-4323)
- Emrah Gecili (ORCID: https://orcid.org/0000-0001-5221-4922)
- Elizabeth A. Disbrow (ORCID: https://orcid.org/0000-0002-2133-3221)
- Taymaz Akan (ORCID: https://orcid.org/0000-0003-4070-1058)
- Steven A. Conrad (ORCID: https://orcid.org/0000-0002-4014-969X)
- Sait Alp (ORCID: https://orcid.org/0000-0003-2462-6166)
- Md. Shenuarin Bhuiyan
- Fatih Gelir
Institutions
- Cincinnati Children's Hospital Medical Center (US)
- Karadeniz Technical University (TR)
- Louisiana State University in Shreveport (US)
- University of Cincinnati (US)
- Louisiana State University Health Sciences Center Shreveport (US)
Publication Details
- Journal
- Journal of Medical and Biological Engineering
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1007/s40846-024-00918-z
- Citations
- 6
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 2.31
Funders
- U.S. Department of Defense
- Foundation for the National Institutes of Health
- Alzheimer's Association
- Bristol-Myers Squibb
- Biogen
- Alzheimer's Disease Neuroimaging Initiative
- BioClinica
- Eisai
- National Institutes of Health