Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

ABSTRACT:

Importance

Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.

Objective

To characterize the algorithmic fairness of longitudinal prediction models for AD progression.

Design, setting, and participants

This prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at l

SUBMITTER: Yuan C 

PROVIDER: S-EPMC10630899 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature