Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.

ABSTRACT:

Background

Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine learning techniques and to explore factors influencing women's choices of delivery locations in East Africa.

Method

The study focused on 86,009 childbearing women in East Africa. A comparative analysis of 12 advanced machine learning algorithms was conducted, utilizing various data balancing techniques and hyperparameter optimization methods to enhance model performance.

Result

The prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorit

SUBMITTER: Ngusie HS 

PROVIDER: S-EPMC11631870 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature