Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer's disease spectrum: demonstration analyses in the COMPASS-ND study.

ABSTRACT:

Background

Research in aging, impairment, and Alzheimer's disease (AD) often requires powerful computational models for discriminating between clinical cohorts and identifying early biomarkers and key risk or protective factors. Machine Learning (ML) approaches represent a diverse set of data-driven tools for performing such tasks in big or complex datasets. We present systematic demonstration analyses to compare seven frequently used ML classifier models and two eXplainable Artificial Intelligence (XAI) techniques on multiple performance metrics for a common neurodegenerative disease dataset. The aim is to identify and characterize the best performing ML and XAI algorithms for the present data.

Method

We accessed a Canadian Consortium on Neurodegeneration in Aging dataset f

SUBMITTER: Fah H 

PROVIDER: S-EPMC11913811 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature