Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning

Background: Attrition is a challenge in parameter estimation in both longitudinal and multi-stage cross-sectional studies. Here, we examine utility of machine learning to predict attrition and identify associated factors in a two-stage population-based epilepsy prevalence study in Nairobi. Methods:...

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Autori principali:	Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Elsevier 2025-06-01
Serie:	Global Epidemiology
Soggetti:	Machine learning Attrition Loss to follow-up Urban settlements Epilepsy Prevalence
Accesso online:	http://www.sciencedirect.com/science/article/pii/S259011332500001X
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