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 APOEɛ4 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.
Keywords: APOE; Aging; Alzheimer’s disease; machine learning; mild cognitive impairment; neuropsychology.