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 Fischer'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

SUBMITTER: Gupta A 

PROVIDER: S-EPMC7369403 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature