{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:57:24Z","timestamp":1790575044849,"version":"4.1.0"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Malaria in pregnancy remains a significant public health concern in sub-Saharan Africa. It may contribute to population-level maternal mortality through adverse maternal outcomes. This study aims to develop a machine learning framework to predict maternal mortality ratio (MMR) across African countries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A supervised machine learning framework was applied to identified open source, country-level data across 52 African countries through (2000\u20132020). MMR was estimated using Support Vector Regression (SVR) model. Model performance was evaluated using a random 70\/30 split of country-year observations, temporal block sensitivity analysis applied using 2000\u20132016 for training and 2017\u20132020 for testing and 5-fold grouped cross-validation in which entire countries were held out from model training.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      MMR was positively correlated with stillbirth rate (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f0.76; 95% CI, 0.65\u20130.84), total fertility rate (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f0.65; 95% CI, 0.51\u20130.76), malaria incidence (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f0.42; 95% CI, 0.24\u20130.58), female malaria prevalence (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f0.41; 95% CI, 0.22\u20130.58), and anemia prevalence in pregnant women (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f0.38; 95% CI, 0.20\u20130.55). Inverse correlations were observed with HDI (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f\u22120.75; 95% CI, \u22120.83 to \u22120.63), female primary completion (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f\u22120.67; 95% CI, \u22120.79 to \u22120.52), and female secondary enrolment (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f\u22120.68; 95% CI, \u22120.77 to \u22120.57). Temporal-holdout permutation importance identified HDI and stillbirth rate as the leading contributors to predictive performance, whereas female malaria prevalence and malaria incidence had smaller positive importances. Country-specific\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      values ranged from \u22120.202 to 0.891, indicating substantial between-country variation in model performance. The SVR model achieved\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      =\u202f0.978, MAE\u202f=\u202f27.4, and RMSE\u202f=\u202f44.1 under the random country-year holdout, and\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      =\u202f0.940, MAE\u202f=\u202f44.6, and RMSE\u202f=\u202f64.7 under temporal validation. When entire countries were held out using 5-fold grouped cross-validation, pooled out-of-fold performance decreased to\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      =\u202f0.524, MAE\u202f=\u202f158.0, and RMSE\u202f=\u202f215.7.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion and recommendations<\/jats:title>\n                    <jats:p>The findings of this study indicate that MMR in African countries is influenced by malaria burden and strongly shaped by reproductive, and socioeconomic factors. Reducing maternal mortality in Africa requires the implementation of comprehensive and effective maternal health policy approach that integrates malaria control strategies, malaria-climate surveillance and maternal health literacy programs, alongside strengthening governance, improving institutional capacity, and enhancing health-system performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1929496","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:45:49Z","timestamp":1790574349000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning\u2013based ecological prediction of maternal mortality ratio in malaria-endemic Africa"],"prefix":"10.3389","volume":"14","author":[{"given":"Messaouda","family":"Belfakir","sequence":"first","affiliation":[{"name":"Institute of Public Health,College of Medicine and Health Sciences, United Arab Emirates University","place":["Al Ain, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imad","family":"Shahid","sequence":"additional","affiliation":[{"name":"Mechanical and Aerospace Engineering Department, College of Engineering, United Arab Emirates University","place":["Al Ain, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Belfkir","sequence":"additional","affiliation":[{"name":"Physics Department, College of Sciences, United Arab Emirates University","place":["Al Ain, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bal\u00e1zs","family":"\u00c1d\u00e1m","sequence":"additional","affiliation":[{"name":"Institute of Public Health,College of Medicine and Health Sciences, United Arab Emirates University","place":["Al Ain, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rami","family":"AlRifai","sequence":"additional","affiliation":[{"name":"Infectious Diseases Epidemiology Research Advancement Unit (IDERA), Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University","place":["Al Ain, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farida","family":"Alhosani","sequence":"additional","affiliation":[{"name":"The Global Institute for Disease Elimination","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdessamad","family":"Tridane","sequence":"additional","affiliation":[{"name":"Mathematical Department, College of Sciences, United Arab Emirates University","place":["Al Ain, United Arab 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