Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023: a machine learning algorithm

Background Birth-related mortality is significantly increased by home births without skilled medical assistance during delivery, presenting a major risk to the public’s health. The objective of this study is to predict home delivery and identify the determinants using machine learning algorithm in s...

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Veröffentlicht in:	BMC public health Jg. 25; H. 1; S. 302 - 25
Hauptverfasser:	Zegeye, Adem Tsegaw, Tilahun, Binyam Chaklu, Fekadie, Makida, Addisu, Eliyas, Wassie, Birhan, Alelign, Berihun, Sharew, Mequannet, Baykemagn, Nebebe Demis, Kebede, Abdulaziz, Yehuala, Tirualem Zeleke
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 24.01.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Adolescent Adult Africa South of the Sahara Algorithms Artificial neural networks Biostatistics Childbirth & labor Childbirth at home Childrens health Datasets Decision trees Demographic and health survey Demographics Demography Education Environmental Health Epidemiology Fatalities Female Health aspects Health care facilities Health Surveys Home births Home Childbirth - statistics & numerical data Home delivery Humans Learning algorithms Machine Learning Maternal & child health Maternal mortality Medicine Medicine & Public Health Methods Middle Aged Mothers Neural networks Patient outcomes Population Prediction models Pregnancy Professionals Public Health Python Regression analysis Risk factors Sub-Saharan Africa Surveys Television Vaccine Variables Women Womens health Young Adult Africa South of the Sahara Ethiopia Sub-Saharan Africa Africa Home delivery Demographic and health survey Sub-Saharan Africa Machine learning Python
ISSN:	1471-2458, 1471-2458
Online-Zugang:	Volltext
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