Machine learning‐based cognitive impairment classification with optimal combination of neuropsychological tests

摘要

An extensive battery of neuropsychological tests is currently used to classify individuals as healthy (HV), mild cognitively impaired (MCI), and with Alzheimer's disease (AD). We used machine learning models for effective cognitive impairment classification and optimized the number of tests for expeditious and inexpensive implementation.