Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
Dementia is a progressive condition that impairs an individual's cognitive
health and daily functioning, with mild cognitive impairment (MCI) often
serving as its precursor. The prediction of MCI to dementia conversion has been
well studied, but previous studies have almost always focused on traditional
Machine Learning (ML) based methods that require sharing sensitive clinical
information to train predictive models. This study proposes a privacy-enhancing
solution using Federated Learning (FL) to train predictive models for MCI to
dementia conversion without sharing sensitive data, leveraging socio
demographic and cognitive measures. We simulated and compared two network
architectures, Peer to Peer (P2P) and client-server, to enable collaborative
learning. Our results demonstrated that FL had comparable predictive
performance to centralized ML, and each clinical site showed similar
performance without sharing local data. Moreover, the predictive performance of
FL models was superior to site specific models trained without collaboration.
This work highlights that FL can eliminate the need for data sharing without
compromising model efficacy.
