Abstract
Background: Identifying individuals at risk for mild cognitive impairment (MCI) is of urgent clinical need. Objective: This study aimed to determine whether machine learning approaches could harness longitudinal neuropsychology measures, medical data, and APOE ϵ4 genotype to identify individuals at risk of MCI 1 to 2 years prior to diagnosis. Methods: Data from 676 individuals who participated in the 'APOE in the Predisposition to, Protection from and Prevention of Alzheimer's Disease' longitudinal study (N = 66 who converted to MCI) were utilized in supervised machine learning algorithms to predict conversion to MCI. Results: A random forest algorithm predicted conversion 1-2 years prior to diagnosis with 97% accuracy (p = 0.0026). The global minima (each individual's lowest score) of memory measures from the 'Rey Auditory Verbal Learning Test' and the 'Selective Reminding Test' were the strongest predictors. Conclusions: This study demonstrates the feasibility of using machine learning to identify individuals likely to convert from normal cognition to MCI.
Original language | English (US) |
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Pages (from-to) | 83-94 |
Number of pages | 12 |
Journal | Journal of Alzheimer's Disease |
Volume | 98 |
Issue number | 1 |
DOIs | |
State | Published - Mar 5 2024 |
Keywords
- APOE
- Aging
- Alzheimer's disease
- machine learning
- mild cognitive impairment
- neuropsychology
ASJC Scopus subject areas
- General Neuroscience
- Clinical Psychology
- Geriatrics and Gerontology
- Psychiatry and Mental health