JMIR medical informatics · 2026
Health Care Access Barriers Among Reproductive-Age Women in East Africa: Development and Validation of Machine Learning Prediction Models Using DHS Data.
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Kelkay Jenberu Mekurianew, Aschalew Andualem Yalew, Teshale Getachew, Jejaw Melak, Demissie Kaleb Assegid, Tadele Azmeraw, Tiruneh Misganaw Guadie, Tafere Tesfahun Zemene, Hagos Asebe, Baykemagn Nebebe Demis
Аннотация
BACKGROUND: The World Health Organization advises that every nation should take responsibility for guaranteeing access to health care 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 health care services.
OBJECTIVE: This study aimed to predict barriers to health services and associated factors among reproductive-aged women in East Africa using machine learning algorithms and identify the best-performing predictive model.
METHODS: Analysis of secondary data from 6 East African countries using the Demographic and Health Surveys 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 were extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. A machine learning algorithm using different classification models was implemented. All analyses and calculations were performed in the Python 3 programming language in Jupyter Notebook using imblearn, scikit-learn, and Extreme Gradient Boosting (XGBoost) packages.
RESULTS: Among 228,654 reproductive-age women included in the study, the XGBoost classifier demonstrated the best predictive performance, with 94.46% accuracy, 94.62% precision, 93.73% recall, 94.17% F1-score, and 98% area under the curve. Overall, 92.63% (211,794/228,654) of women experienced barriers to health care access. Predictors of barriers to health access were identified using an XGBoost model with the help of Shapley Additive Explanations. This study showed that maternal occupation, maternal age, low media exposure, parity, marital status, health insurance, and community literacy were the top predicting factors of barriers to health access.
CONCLUSIONS: The XGBoost model demonstrated the best predictive performance among the evaluated algorithms. The findings indicate that a substantial proportion of reproductive-age women experience barriers to health care access, although no formal subnational "extreme risk" classification was conducted in this study. Enhancing comprehensive health education and reducing financial barriers through the expansion of health insurance coverage may help improve health care access, particularly among vulnerable populations such as rural women.
OBJECTIVE: This study aimed to predict barriers to health services and associated factors among reproductive-aged women in East Africa using machine learning algorithms and identify the best-performing predictive model.
METHODS: Analysis of secondary data from 6 East African countries using the Demographic and Health Surveys 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 were extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. A machine learning algorithm using different classification models was implemented. All analyses and calculations were performed in the Python 3 programming language in Jupyter Notebook using imblearn, scikit-learn, and Extreme Gradient Boosting (XGBoost) packages.
RESULTS: Among 228,654 reproductive-age women included in the study, the XGBoost classifier demonstrated the best predictive performance, with 94.46% accuracy, 94.62% precision, 93.73% recall, 94.17% F1-score, and 98% area under the curve. Overall, 92.63% (211,794/228,654) of women experienced barriers to health care access. Predictors of barriers to health access were identified using an XGBoost model with the help of Shapley Additive Explanations. This study showed that maternal occupation, maternal age, low media exposure, parity, marital status, health insurance, and community literacy were the top predicting factors of barriers to health access.
CONCLUSIONS: The XGBoost model demonstrated the best predictive performance among the evaluated algorithms. The findings indicate that a substantial proportion of reproductive-age women experience barriers to health care access, although no formal subnational "extreme risk" classification was conducted in this study. Enhancing comprehensive health education and reducing financial barriers through the expansion of health insurance coverage may help improve health care access, particularly among vulnerable populations such as rural women.