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

Abstract

Introduction: 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. Methods: Using random forests (RF) and support vector machine, we classified cognitive impairment in multi-class data sets from Rush Religious Orders Study Memory and Aging Project, and National Alzheimer's Coordinating Center. We applied Fisher's linear discrimination and assessed importance of each test iteratively for feature selection. Results: RF has best accuracy with increased sensitivity, specificity in this first ever multi-class classification of HV, MCI, and AD. Moreover, a subset of six to eight tests shows equivalent classification accuracy as an entire battery of tests. Discussions: Fully automated feature selection approach reveals six to eight tests comprising episodic, semantic memory, perceptual orientation, and executive functioning can accurately classify the cognitive status, ensuring minimal subject burden.