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dc.contributor.authorNjagi, Calvince O
dc.date.accessioned2023-11-21T07:46:46Z
dc.date.available2023-11-21T07:46:46Z
dc.date.issued2022
dc.identifier.urihttp://erepository.uonbi.ac.ke/handle/11295/164115
dc.description.abstractIn 2016,the World Health Organization(WHO)updated the prior recommendations for Antenatal Care(ANC)visitsandsuggested8+ANC appointments over the course of pregnancy.The aim of this was to address the problem of perinatal deaths and improve maternal health.However,the reality is that the uptake of ANC visits is still very lowas pregnant women do not adhere to this guideline.As a result,the initial step in resolving the issue could be to work toward meeting the goal off our ANC visits that was initially set.This can be accomplished by first gaining an understanding of the factors that cause women to not adhere to the established guidelines,which ultimately results in a low number of pregnant women using the service...en_US
dc.language.isoenen_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titlePredicting Antenatal Care (ANC) Visits Using Machine Learning Algorithmsen_US
dc.typeThesisen_US


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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States