{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T17:03:32Z","timestamp":1789664612425,"version":"4.0.1"},"reference-count":18,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>This cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019\u20132020) involving 14,634 women. K-fold cross-validation (k = 10) and synthetic minority oversampling technique (SMOTE) were used to manage dataset partitioning and class imbalance. Descriptive and multivariate analyses were conducted to identify the prevalence and risk factors for adverse pregnancy outcomes. Seven machine learning algorithms were assessed for their accuracy, precision, recall, F1 score, and area under the curve (AUC).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Of the pregnancies analyzed, 93.4% resulted in live births, while 4.5% ended in miscarriage, and 2.1% in stillbirth. Advanced maternal age(&gt;30 years),women aged 30\u201334 years (adjusted odds ratio [AOR] = 5.755; 95% confidence interval [CI] = 3.085\u201310.074; p &lt; 0.001), 35\u201339 years (AOR = 8.458; 95% CI = 4.507\u201310.571; p &lt; 0.001), 40\u201344 years (AOR = 11.86; 95% CI = 6.250\u201321.842; p &lt; 0.001), and 45\u201349 years (AOR = 14.233; 95% CI = 7.359\u201325.922; p &lt; 0.001), compared to those aged 15\u201319 years, and multiple unions (polyandry) (AOR = 1.320; 95% CI = 1.104\u20131.573, p = 0.002), and women not visited by healthcare provider during pregnancy (AOR = 1.421; 95%CI = 1.300\u20131.611, p&lt;0.001) were factors associated with an increased risk of adverse pregnancy outcomes. In contrast, being married (AOR = 0.894; 95% CI = 0.787\u20130.966) and attending at least two antenatal care (ANC) visits (AOR = 0.801; 95% CI = 0.664\u20130.961) were linked to reduced risk. The K-nearest neighbors (KNN) model outperformed other ML Models in predicting adverse pregnancy outcomes, achieving 86% accuracy, 89% precision, 97% recall, 93% F1 score, and an area under the curve (AUC) of 0.842. The ML models constantly highlighted that woman with advanced maternal age, those in multiple unions, and inadequate ANC were more susceptible to adverse pregnancy outcomes.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Machine learning algorithms, particularly KNN, are effective in predicting adverse pregnancy outcomes, facilitating early intervention and improved maternal and neonatal care.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0312447","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T19:20:38Z","timestamp":1733426438000},"page":"e0312447","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Predicting adverse pregnancy outcome in Rwanda using machine learning techniques"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6495-5719","authenticated-orcid":true,"given":"Theogene","family":"Kubahoniyesu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignace Habimana","family":"Kabano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"pone.0312447.ref001","article-title":"Maternal Health","author":"UNFPA","year":"2023"},{"key":"pone.0312447.ref002","article-title":"Maternal mortality","author":"WHO","year":"2024"},{"key":"pone.0312447.ref003","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1624\/105812409X461225","article-title":"Safe, Healthy Birth: What Every Pregnant Woman Needs to Know.","volume":"18","author":"JA Lothian","year":"2009","journal-title":"J Perinat Educ."},{"key":"pone.0312447.ref004","article-title":"Pregnancy-related complications","author":"WHO","year":"2022"},{"key":"pone.0312447.ref005","article-title":"Abortion care guideline","author":"WHO","year":"2022"},{"key":"pone.0312447.ref006","article-title":"Abortion","author":"WHO","year":"2021"},{"key":"pone.0312447.ref007","article-title":"Preterm birth","author":"WHO","year":"2022"},{"key":"pone.0312447.ref008","article-title":"A neglected tragedy: the global burden of stillbirths","author":"WHO","year":"2020"},{"key":"pone.0312447.ref009","unstructured":"RDHS. 6th Rwanda Demographic and Health Survey, 2019\u20132020 (RDHS -VI) [Internet]. Kigali; 2020. Available from: https:\/\/www.statistics.gov.rw\/datasource\/demographic-and-health-survey-dhs"},{"key":"pone.0312447.ref010","doi-asserted-by":"crossref","DOI":"10.1186\/s12884-022-04594-2","article-title":"Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.","volume":"22","author":"MN Islam","year":"2022","journal-title":"BMC Pregnancy Childbirth"},{"key":"pone.0312447.ref011","first-page":"1","article-title":"Machine Learning: Algorithms, Real \u2011 World Applications and Research Directions.","volume":"2","author":"IH Sarker","year":"2021","journal-title":"SN Comput Sci [Internet]."},{"key":"pone.0312447.ref012","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.procs.2015.08.573","article-title":"Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining.","author":"S Pereira","year":"2015","journal-title":"Procedia Comput Sci. Elsevier B.V."},{"key":"pone.0312447.ref013","article-title":"Rwanda Population and Housing Census, 5th RPHC","author":"NISR","year":"2022"},{"key":"pone.0312447.ref014","doi-asserted-by":"crossref","DOI":"10.1186\/s12889-023-15235-4","article-title":"Age-specific effects on adverse pregnancy outcomes vary by maternal characteristics: a population-based retrospective study in Xiamen, China.","volume":"23","author":"C Huang","year":"2023","journal-title":"BMC Public Health."},{"key":"pone.0312447.ref015","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.whi.2011.04.001","article-title":"Maternal marital status and the risk of stillbirth and infant death: A population-based cohort study on 40 million births in the united states.","volume":"21","author":"J Balayla","year":"2021","journal-title":"Women\u2019s Heal Issues"},{"key":"pone.0312447.ref016","first-page":"531","article-title":"Associations Between Early First Sexual Intercourse and Later Sexual and Reproductive Outcomes: A Systematic Review of Population- Based Data.","author":"W Heywood","year":"2015"},{"key":"pone.0312447.ref017","first-page":"55","article-title":"Independent and joint contributions of inadequate antenatal care timing, contacts and content to adverse pregnancy outcomes.","author":"M Saaka","year":"2023","journal-title":"Ann Med."},{"key":"pone.0312447.ref018","first-page":"23","article-title":"Prevalence and correlates of adolescent pregnancy, motherhood and adverse pregnancy outcomes in Uttar Pradesh and Bihar.","author":"N Shri","year":"2023","journal-title":"BMC Pregnancy Childbirth."}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0312447","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T19:20:50Z","timestamp":1733426450000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0312447"}},"subtitle":[],"editor":[{"given":"Obasanjo","family":"Afolabi Bolarinwa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"references-count":18,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12,5]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0312447","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,5]]}}}