Identifying Predictors of Cervical Cancer Screening Uptake in Sub-Saharan Africa Using Machine Learning: Cross-Sectional Study

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Vydáno v:	JMIR Public Health and Surveillance vol. 11 (2025), p. e71677-e71691
Hlavní autor:	Nebebe, Demis Baykemagn
Další autoři:	Aweke, Mekuriaw Nibret, Amare Mesfin, Lemlem, Daniel Baffa, Muluken Chanie Agimas, Abuhay, Habtamu Wagnew, Dagnew Getnet Adugna, Tewodros Getaneh Alemu, Alemu Teshale Bicha, Gebrie Getu Alemu
Vydáno:	JMIR Publications
Témata:	Africa Sub-Saharan Africa Smartphones Vaccines Accuracy Womens health Datasets Health surveys Cancer therapies Data processing Developing countries > LDCs Industrialized nations Human papillomavirus Efficiency Machine learning Sexual behavior Rural areas Decision making Neural networks Public health Socioeconomics Cervical cancer Sexually transmitted diseases > STD Algorithms Decision trees Medical screening Prediction models Social
On-line přístup:	Citation/Abstract Full Text + Graphics Full Text - PDF
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