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

• Published in: BMC public health Vol. 25; no. 1; pp. 302 - 25
• Main Authors: 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
• Language: English
• Published: London BioMed Central 24.01.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: 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
• DOI: 10.1186/s12889-025-21334-1

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.