NDLI: 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

Content Provider	Springer Nature : BioMed Central
Author	Zegeye, Adem Tsegaw Tilahun, Binyam Chaklu Fekadie, Makida Addisu, Eliyas Wassie, Birhan Alelign, Berihun Sharew, Mequannet Baykemagn, Nebebe Demis Kebede, Abdulaziz Yehuala, Tirualem Zeleke
Abstract	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 sub-Saharan African. Methods This study used design science approaches. The data set obtained from demographic health survey in sub-Saharan African weighted sample of 299,759 women was included in the stud. Machine learning models such as Random Forest, Decision Tree, K-Nearest Neighbor, Logistic Regression, Extreme Gradient Boosting, AdaBoost, Artificial Neural Network, and Naive Bayes were used. The predictive model was evaluated by area under the curve, accuracy, precision, recall, and F-measure. Results The final experimentation results indicated that random forest model performed the best to predict home delivery with accuracy (83%) and, ROC curve (89%). The Shapley additive explanation features an importance plot optimized for random forest model to identifying the most predictors of home delivery. Association rules findings showed that inadequate antenatal care visits, marital status married, no education, mobile phone, television, electricity, poor wealth index, infrequent television viewing, and rural residence were predictor of home delivery. Conclusion The random forest machine learning model provides greater predictive power for estimating home delivery risk factors. To reduce the prevalence of home delivery, this finding recommends to emphasis on improving antenatal care services, education, and awareness about health facility delivery.
Related Links	https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-025-21334-1.pdf
Ending Page	25
Page Count	25
Starting Page	1
File Format	HTM / HTML
ISSN	14712458
DOI	10.1186/s12889-025-21334-1
Journal	BMC Public Health
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2025-01-24
Access Restriction	Open
Subject Keyword	Public Health Medicine Epidemiology Biostatistics Vaccine Environmental Health Home delivery Sub-Saharan Africa Machine learning Demographic and health survey Python Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.9/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Determinants of adverse birth outcome in Sub-Saharan Africa: analysis of recent demographic and health surveys

Determinants of early marriage among married women in nine high fertility sub-Saharan African countries: a multilevel analysis of recent demographic and health surveys

Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis

Determinants of institutional delivery in Sub-Saharan Africa: findings from Demographic and Health Survey (2013–2017) from nine countries

Three in four children age 12–23 months missed opportunities for vaccination in Sub-Saharan African countries: a multilevel mixed effect analysis of demographic health and surveys 2016–2023

Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques

Overweight and obesity among women: analysis of demographic and health survey data from 32 Sub-Saharan African Countries

Animal source food consumption and its determinants among children aged 6 to 23 months in sub-Saharan African countries: a multilevel analysis of demographic and health survey

Four in ten married women demands satisfied by modern contraceptives in high fertility sub-Saharan Africa countries: a multilevel analysis of demographic and health surveys

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

Similar Documents

Determinants of adverse birth outcome in Sub-Saharan Africa: analysis of recent demographic and health surveys

Determinants of early marriage among married women in nine high fertility sub-Saharan African countries: a multilevel analysis of recent demographic and health surveys

Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis

Determinants of institutional delivery in Sub-Saharan Africa: findings from Demographic and Health Survey (2013–2017) from nine countries

Three in four children age 12–23 months missed opportunities for vaccination in Sub-Saharan African countries: a multilevel mixed effect analysis of demographic health and surveys 2016–2023

Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques

Overweight and obesity among women: analysis of demographic and health survey data from 32 Sub-Saharan African Countries

Animal source food consumption and its determinants among children aged 6 to 23 months in sub-Saharan African countries: a multilevel analysis of demographic and health survey

Four in ten married women demands satisfied by modern contraceptives in high fertility sub-Saharan Africa countries: a multilevel analysis of demographic and health surveys

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