Modelling seizure-related predictors of epilepsy diagnostic gap in two urban informal settlements of Nairobi using machine learning.

ABSTRACT:

Background

There is a wide gap in epilepsy diagnosis, particularly in low- and middle-income countries. We used machine learning models to identify seizure-related factors associated with the epilepsy diagnostic gap within the Nairobi Urban Health and Demographic Surveillance System (NUHDSS), Kenya, to inform effective community-level interventions.

Methods

Data were drawn from a two-stage, population-based census. In Stage-I, 56,425 residents of NUHDSS were screened for possible convulsive and non-convulsive epilepsy using a standardized questionnaire. In Stage-II, individuals who screened positive were invited for clinical assessment and diagnostic confirmation by neurologists. We used latent class analysis to classify symptom patterns. Seven machine learning models were t

SUBMITTER: Mwanga D 

PROVIDER: S-EPMC12828424 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature