Leveraging machine learning to predict mosquito bed net utilization among women of reproductive age in sub-Saharan Africa

Abstract Background Malaria remains a major public health challenge, particularly in sub-Saharan Africa, where women of reproductive age are especially vulnerable during pregnancy and childbirth. To identify key predictors and improve predictive accuracy, machine learning algorithms such as Random F...

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Detaylı Bibliyografya
Asıl Yazarlar:	Nebebe Demis Baykemagn, Tesfahun Zemene Tafere, Getachew Teshale, Andualem Yalew Aschalew, Melak Jejaw, Kaleb Assegid Demissie, Azmeraw Tadele, Asebe Hagos, Misganaw Guadie Tiruneh, Jenberu Mekurianew Kelkay
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	BMC 2025-10-01
Seri Bilgileri:	Malaria Journal
Konular:	Prediction Women of reproductive age Machine learning Malaria Mosquito net utilization
Online Erişim:	https://doi.org/10.1186/s12936-025-05563-8
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Leveraging machine learning to predict mosquito bed net utilization among women of reproductive age in sub-Saharan Africa

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