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

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 ɛ4 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.