Modeling the Determinants of Attrition in a Two-stage Epilepsy Prevalence Survey in Nairobi Using Machine Learning

dc.contributor.author	Mwanga D. M.
dc.contributor.author	Kipchirchir I. C.
dc.contributor.author	Muhua G. O.
dc.contributor.author	Newton C. R.
dc.contributor.author	Kadengye D. T.
dc.date.accessioned	2025-07-24T07:23:19Z
dc.date.available	2025-07-24T07:23:19Z
dc.date.issued	2025
dc.identifier.uri	https://doi.org/10.1016/j.gloepi.2025.100183
dc.identifier.uri	http://knowhub.aphrc.org/handle/123456789/2395
dc.description.abstract	This study applies machine learning (including random forest, XGBoost, SVM, and Super Learner) to predict attrition in a two-stage epilepsy prevalence survey in Nairobi. Results show high model performance (AUC up to 0.98), identifying key predictors such as proximity to industrial areas, gender, employment, education, household size, and seizure history. The findings guide targeted strategies to improve follow-up rates and inform a predictive tool for future surveys.
dc.publisher	Elsevier
dc.subject	Epidemiology II Public Health II Machine Learning II Epilepsy II Urban Health II
dc.title	Modeling the Determinants of Attrition in a Two-stage Epilepsy Prevalence Survey in Nairobi Using Machine Learning

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