Academic Journal
Health Care Access Barriers Among Reproductive-Age Women in East Africa: Development and Validation of Machine Learning Prediction Models Using DHS Data.
| Τίτλος: | 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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| Συγγραφείς: | Kelkay JM; Department of Public Health, College of Health Sciences, Debark University, Gondar, Debark, 6000, Ethiopia, 251 0947088025., Aschalew AY; Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Teshale G; Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Jejaw M; Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Demissie KA; Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Tadele A; Department of Medical Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Tiruneh MG; Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Tafere TZ; Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Hagos A; Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Baykemagn ND; Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. |
| Πηγή: | JMIR medical informatics [JMIR Med Inform] 2026 Sep 28; Vol. 14, pp. e85695. Date of Electronic Publication: 2026 Sep 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101645109 Publication Model: Electronic Cited Medium: Internet ISSN: 2291-9694 (Electronic) Linking ISSN: 22919694 NLM ISO Abbreviation: JMIR Med Inform Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Toronto : JMIR Publications, [2013]- |
| Ιατρικοί όροι (MeSH): | Health Services Accessibility*/statistics & numerical data , Health Services Accessibility*/standards , Boosting Machine Learning Algorithms*, Adolescent ; Adult ; Female ; Humans ; Middle Aged ; Young Adult ; Africa, Eastern ; Classification Algorithms ; East African People ; Predictive Learning Models ; Secondary Data Analysis |
| Περίληψη: | 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% F 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. (© Jenberu Mekurianew Kelkay, Andualem Yalew Aschalew, Getachew Teshale, Melak Jejaw, Kaleb Assegid Demissie, Azmeraw Tadele, Misganaw Guadie Tiruneh, Tesfahun Zemene Tafere, Asebe Hagos, Nebebe Demis Baykemagn. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).) |
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| Contributed Indexing: | Keywords: East Africa; Extreme Gradient Boosting; XGBoost; barriers to health care access; machine learning; predictive modeling; reproductive-age women |
| Entry Date(s): | Date Created: 20260928 Date Completed: 20260928 Latest Revision: 20260930 |
| Update Code: | 20260930 |
| PubMed Central ID: | PMC13618409 |
| DOI: | 10.2196/85695 |
| PMID: | 42804159 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2291-9694 |
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| DOI: | 10.2196/85695 |