Evaluating the generalisability of region-naive machine learning algorithms for the identification of epilepsy in low-resource settings.

ABSTRACT:

Objectives

Approximately 80% of people with epilepsy live in low- and middle-income countries (LMICs), where limited resources and stigma hinder accurate diagnosis and treatment. Clinical machine learning models have demonstrated substantial promise in supporting the diagnostic process in LMICs by aiding in preliminary screening and detection of possible epilepsy cases without relying on specialised or trained personnel. How well these models generalise to naïve regions is, however, underexplored. Here, we use a novel approach to assess the suitability and applicability of such clinical tools to aid screening and diagnosis of active convulsive epilepsy in settings beyond their original training contexts.

Methods

We sourced data from the Study of Epidemiology of Epilepsy in D

SUBMITTER: Duta I 

PROVIDER: S-EPMC11819582 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature