Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Oct 11, 2025
Date Accepted: Jul 7, 2026
Healthcare Access Barriers Among Reproductive-Age Women in East Africa: An XGBoost-Based Machine Learning Analysis of DHS Data
ABSTRACT
Background:
The World Health Organization advises that every nation should take responsibility for guaranteeing access to healthcare services as a basic human right. However, due to financial constraints and geographical hurdles, only around half of the population in Africa has access to contemporary healthcare services.
Objective:
Therefore, this study aimed to predict barriers of health services and associated factors among reproductive aged-women in East Africa using a novel framework based on a Stacking Machine Learning Approach.
Methods:
Analysis of secondary data from six East African countries using Demographic and Health Survey from 2016 to the recent 2023 was performed. A weighted total sample of 228,654 women of reproductive age was included in this study. Data have been extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. A stacking Machine learning algorithm using different classification models were implemented. All analysis and calculation were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, and xgboost packages.
Results:
XGBoost classifier demonstrated the best performance with an accuracy of (94.46%), precision of (94.62%), recall of (93.73%) and F1-score of (94.17%). Predictors of barriers to health access are identified using XGBoost model with help Shapely additive eXplanation. This study showed that the maternal occupation, maternal age, media exposure, parity, marital status, health insurance and community literacy were the top predicting factors of barriers to health access.
Conclusions:
XGBoost model was best predictive models with improved performance. More than nine in ten women of reproductive age had no access to healthcare in extremely high-risk areas. Enhancing comprehensive health education and reducing financial burdens through the expansion of health insurance coverage may help address barriers to healthcare access, particularly among reproductive-age women in rural areas Clinical Trial: No
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