Machine Learning Predicts Conversion from Normal Aging to Mild Cognitive Impairment Using Medical History, APOE Genotype, and Neuropsychological Assessment.

Abstract

Identifying individuals at risk for mild cognitive impairment (MCI) is of urgent clinical need. 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. 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. 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. This study demonstrates the feasibility of using machine learning to identify individuals likely to convert from normal cognition to MCI.