Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion
It addresses privacy concerns in healthcare for clinical sites by enabling collaborative model training without data sharing, though it is incremental as it applies an existing method to a new domain.
This study tackled the problem of predicting mild cognitive impairment to dementia conversion by proposing a privacy-enhancing solution using Federated Learning, which achieved comparable predictive performance to centralized machine learning without sharing sensitive data.
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.