{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T08:55:05Z","timestamp":1769158505103,"version":"3.49.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Each year, approximately 2.5 million newborns die globally, with developing countries bearing the impact of this crisis. Sub-Saharan Africa has the highest neonatal mortality rate, with Ethiopia facing alarmingly high figures, particularly in rural areas where mortality is significantly higher due to poor healthcare access and socio-economic challenges.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>This study aimed to develop a predictive model for neonatal mortality in rural Ethiopia using secondary data from the Ethiopian Demographic and Health Surveys (2000\u20132019). The dataset included 29,048 instances and 22 relevant features, which were preprocessed to handle missing values and balance the class distribution using the Synthetic Minority oversampling technique. Several ensemble machine-learning algorithms, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and CatBoost, were applied to build the model. Additionally, the logistic regression algorithm was employed to enhance transparency and interpretability and for comparative analysis. Model performance was evaluated based on accuracy, precision, recall, F1 score, and Receiver Operating Characteristic\u2014Area Under the Curve.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Among the algorithms tested, categorical boosting achieved the highest performance with 97.5% accuracy, 97.52% precision, 97.5% recall, 97.5% F1 score, and an exceptional Receiver Operating Characteristic\u2014Area Under the Curve value of 99.57%. Key risk factors identified include BCG vaccination status, the number of under-five children in the household, recent diarrhea episodes, and iron tablet intake during pregnancy. Valuable feedbacks from community health workers were provided on these factors, helping to refine their impact on neonatal mortality.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>This study developed an effective predictive model for neonatal mortality in rural Ethiopia, providing actionable insights for targeted interventions. The model underscores the importance of improving healthcare access, maternal health, and policy reforms, with the potential to reduce neonatal mortality through mobile health apps and policymaker collaboration.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s44163-025-00305-w","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T12:37:00Z","timestamp":1755866220000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting neonatal mortality using ensemble machine learning algorithms in the case of Ethiopian Rural Areas"],"prefix":"10.1007","volume":"5","author":[{"given":"Melaku Alelign","family":"Mengstie","sequence":"first","affiliation":[]},{"given":"Misganaw Telake","family":"Telele","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"305_CR1","first-page":"1","volume":"6","author":"ZT Tessema","year":"2020","unstructured":"Tessema ZT, Tesema GA. Incidence of neonatal mortality and its predictors among live births in Ethiopia: gompertz gamma shared frailty model. Ital J Pediatr. 2020;6:1\u201310.","journal-title":"Ital J Pediatr"},{"key":"305_CR2","doi-asserted-by":"publisher","DOI":"10.2203\/IJN.2023.70102.2360","author":"G Kaweti","year":"2024","unstructured":"Kaweti G, Tamirat A, Feleke T. Factors predicting treatment outcome of neonatal sepsis in Hawassa university comprehensive specialized hospital, southern Ethiopia: a retrospective cohort study. Iran J Neonatal. 2024. https:\/\/doi.org\/10.2203\/IJN.2023.70102.2360.","journal-title":"Iran J Neonatal"},{"key":"305_CR3","unstructured":"WHO. WHO. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/newborn-mortality,14 March 2024."},{"key":"305_CR4","doi-asserted-by":"publisher","DOI":"10.1080\/14767058.2020.1718093","author":"A Belachew","year":"2020","unstructured":"Belachew A, Tewabe T, Dessie G. Neonatal mortality and its association with antenatal care visits among live births in Ethiopia: a systematic review and meta-analysis. J Matern Neonatal Med. 2020. https:\/\/doi.org\/10.1080\/14767058.2020.1718093.","journal-title":"J Matern Neonatal Med"},{"key":"305_CR5","unstructured":"Building on Ethiopia's success to accelerate survival of mothers and newborns. No. December, 2022."},{"key":"305_CR6","unstructured":"Ethiopian Public Health Institute (EPHI) and ICF, Ethiopia Mini Demographic and Health Survey 2019: Final Report. 2021. https:\/\/dhsprogram.com\/pubs\/pdf\/FR363\/FR363.pdf"},{"key":"305_CR7","doi-asserted-by":"publisher","DOI":"10.1136\/bmjpo-2023-001897","author":"TT Tamir","year":"2023","unstructured":"Tamir TT, et al. Prevalence and determinants of early neonatal mortality in Ethiopia: findings from the Ethiopian Demographic and Health Survey 2016. BMJ Paediatrics Open. 2023. https:\/\/doi.org\/10.1136\/bmjpo-2023-001897.","journal-title":"BMJ Paediatrics Open"},{"key":"305_CR8","unstructured":"D. K. Rathore and P. K. Mannepalli. ISSN\u202f: 2581\u20133404 (Online) Recent Trends in Machine Learning for Health Care Sector ISSN: 2581\u20133404 (Online),\u201d vol. 3404, no. 2, pp. 24\u201329, 2021."},{"issue":"6","key":"305_CR9","first-page":"3549","volume":"120","author":"B Chandramohan","year":"2018","unstructured":"Chandramohan B. Prediction and prevention of domestic violence from social big data using machine learning approach. Int J Pure Appl Math. 2018;120(6):3549\u201361.","journal-title":"Int J Pure Appl Math"},{"key":"305_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11080695","author":"J Hsu","year":"2021","unstructured":"Hsu J, et al. Machine learning approaches to predict in-hospital mortality among neonates with clinically suspected sepsis in the neonatal intensive care unit. J Personal Med. 2021. https:\/\/doi.org\/10.3390\/jpm11080695.","journal-title":"J Personal Med"},{"issue":"19","key":"305_CR11","doi-asserted-by":"publisher","first-page":"9729","DOI":"10.3390\/app12199729","volume":"12","author":"U Park","year":"2022","unstructured":"Park U, Kang Y, Lee H, Yun S. A stacking heterogeneous ensemble learning method for the prediction of building construction project costs. Appl Sci. 2022;12(19):9729.","journal-title":"Appl Sci"},{"issue":"September","key":"305_CR12","doi-asserted-by":"publisher","first-page":"99129","DOI":"10.1109\/ACCESS.2022.3207287","volume":"10","author":"ID Mienye","year":"2022","unstructured":"Mienye ID, Sun Y, Member S. A survey of ensemble learning: concepts, algorithms, applications, and prospects. IEEE Access. 2022;10(September):99129\u201349. https:\/\/doi.org\/10.1109\/ACCESS.2022.3207287.","journal-title":"IEEE Access"},{"key":"305_CR13","doi-asserted-by":"publisher","unstructured":"C. Series, \u201cExtreme gradient boosting method in making forecasting application and analysis of USD exchange rates against rupiah,\u201d pp. 0\u201311, 2021, https:\/\/doi.org\/10.1088\/1742-6596\/1722\/1\/012016.","DOI":"10.1088\/1742-6596\/1722\/1\/012016"},{"key":"305_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11234015","author":"D Salcedo","year":"2022","unstructured":"Salcedo D, et al. Machine learning algorithms application in COVID-19 disease: a systematic literature review and future directions. Electron. 2022. https:\/\/doi.org\/10.3390\/electronics11234015.","journal-title":"Electron"},{"key":"305_CR15","unstructured":"H. I. Classification, \u201cA Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges,\u201d 2023."},{"issue":"5","key":"305_CR16","first-page":"272","volume":"9","author":"J Ali","year":"2012","unstructured":"Ali J, Khan R, Ahmad N, Maqsood I. Random forests and decision trees. Int J Comput Sci Issues. 2012;9(5):272\u20138.","journal-title":"Int J Comput Sci Issues"},{"key":"305_CR17","first-page":"6638","volume":"2018","author":"L Prokhorenkova","year":"2018","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. Catboost: unbiased boosting with categorical features. Adv Neural Inf Process Syst. 2018;2018:6638\u201348.","journal-title":"Adv Neural Inf Process Syst"},{"key":"305_CR18","doi-asserted-by":"crossref","unstructured":"M. A. Ganaie, M. Hu, M. Tanveer*, and P. N. Suganthan*, \u201cEnsemble deep learning: A review,\u201d 2021.","DOI":"10.1016\/j.engappai.2022.105151"},{"key":"305_CR19","volume-title":"A light gradient boosting machine regression model for prediction of agriculture insurance","author":"P Syam","year":"2022","unstructured":"Syam P, Chand S, Divya G. A light gradient boosting machine regression model for prediction of agriculture insurance. Amsterdam: IOS Press; 2022."},{"issue":"1","key":"305_CR20","doi-asserted-by":"publisher","first-page":"12","DOI":"10.11613\/BM.2014.003","volume":"24","author":"S Sperandei","year":"2014","unstructured":"Sperandei S. Understanding logistic regression analysis. Biochem Medica. 2014;24(1):12\u20138. https:\/\/doi.org\/10.11613\/BM.2014.003.","journal-title":"Biochem Medica"},{"key":"305_CR21","doi-asserted-by":"crossref","unstructured":"S. Henning, W. Beluch, A. Fraser, and A. Friedrich, \u201cA Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing,\u201d no. 2022, pp. 523\u2013540, 2023.","DOI":"10.18653\/v1\/2023.eacl-main.38"},{"key":"305_CR22","doi-asserted-by":"publisher","unstructured":"O. Access, \u201cSynthetic Over Sampling Methods for Handling Class Imbalanced Problems\u202f: A Review SMOTE,\u201d 2017, https:\/\/doi.org\/10.1088\/1755-1315\/5.","DOI":"10.1088\/1755-1315\/5"},{"key":"305_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-106","author":"R Blagus","year":"2013","unstructured":"Blagus R, Lusa L. Open access SMOTE for high-dimensional class-imbalanced data. BMC Bioinform. 2013. https:\/\/doi.org\/10.1186\/1471-2105-14-106.","journal-title":"BMC Bioinform"},{"key":"305_CR24","unstructured":"C. Smote and T. Links, \u201cImbalanced Classification in Python: SMOTE- Tomek Links Method\u201d,. - Google Search,\u201d pp. 1\u201313."},{"key":"305_CR25","doi-asserted-by":"publisher","unstructured":"B. H. Shekar and G. Dagnew, \u201cGrid search-based hyperparameter tuning and classification of microarray cancer data,\u201d 2019 2nd Int. Conf. Adv. Comput. Commun. Paradig. ICACCP 2019, no. February, pp. 1\u20138, 2019, https:\/\/doi.org\/10.1109\/ICACCP.2019.8882943","DOI":"10.1109\/ICACCP.2019.8882943"},{"issue":"12","key":"305_CR26","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1456\/IJACSA.2018.091271","volume":"9","author":"MH Mohd Yusof","year":"2018","unstructured":"Mohd Yusof MH, Mokhtar MR, Zain AM, Maple C. Embedded feature selection method for a network-level behavioural analysis detection model. Int J Adv Comput Sci Appl. 2018;9(12):509\u201317. https:\/\/doi.org\/10.1456\/IJACSA.2018.091271.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"305_CR27","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.protcy.2012.02.068","volume":"1","author":"EM Karabulut","year":"2012","unstructured":"Karabulut EM, \u00d6zel SA, \u0130brik\u00e7i T. A comparative study on the effect of feature selection on classification accuracy. Procedia Technol. 2012;1:323\u20137. https:\/\/doi.org\/10.1016\/j.protcy.2012.02.068.","journal-title":"Procedia Technol"},{"key":"305_CR28","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai J, Luo J, Wang S, Yang S. Feature selection in machine learning: a new perspective. Neurocomputing. 2018;300:70\u20139. https:\/\/doi.org\/10.1016\/j.neucom.2017.11.077.","journal-title":"Neurocomputing"},{"issue":"2","key":"305_CR29","doi-asserted-by":"publisher","first-page":"196","DOI":"10.33480\/jitk.v9i2.5015","volume":"9","author":"AM Priyatno","year":"2024","unstructured":"Priyatno AM, Widiyaningtyas T. a Systematic literature review: recursive feature elimination algorithms. JITK. 2024;9(2):196\u2013207. https:\/\/doi.org\/10.33480\/jitk.v9i2.5015.","journal-title":"JITK"},{"issue":"1","key":"305_CR30","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1159\/ijphs.v13i1.22577","volume":"13","author":"M Raihan","year":"2024","unstructured":"Raihan M, Saha PK, Das Gupta R, Kabir AZMT. A deep learning and machine learning approach to predict neonatal death in the context of S\u00e3o Paulo. Int J Public Health Sci (IJPHS). 2024;13(1):179\u201390. https:\/\/doi.org\/10.1159\/ijphs.v13i1.22577.","journal-title":"Int J Public Health Sci (IJPHS)"},{"key":"305_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01497-8","volume":"8","author":"A Sheikhtaheri","year":"2021","unstructured":"Sheikhtaheri A, Zarkesh MR, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: development and validation of machine learning models. BMC Med Inform Decis Mak. 2021;8:1\u201314. https:\/\/doi.org\/10.1186\/s12911-021-01497-8.","journal-title":"BMC Med Inform Decis Mak"},{"key":"305_CR32","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/3567194","author":"YG Robi","year":"2023","unstructured":"Robi YG, Sitote TM. Neonatal disease prediction using machine learning techniques. J Healthc Eng. 2023. https:\/\/doi.org\/10.1155\/2023\/3567194.","journal-title":"J Healthc Eng"},{"issue":"7","key":"305_CR33","first-page":"583","volume":"10","author":"Z Kefi","year":"2019","unstructured":"Kefi Z, Aloui K, Naceur MS. New approach based on machine learning for short-term mortality prediction in neonatal intensive care unit. Int J Adv Comput Sci Appl. 2019;10(7):583\u201391.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"305_CR34","unstructured":"A. T. Prima, N. T. Thity, and R. Rois, \u201cClinical Images and Medical Case Reports Risk predictors selection and predict for the first-day neonatal mortality in Bangladesh using machine learning techniques,\u201d 2022."},{"key":"305_CR35","doi-asserted-by":"publisher","DOI":"10.1515\/biol-2022-0643","author":"MI Satti","year":"2023","unstructured":"Satti MI, Ali MW, Irshad A, Shah MA. Studying infant mortality: a demographic analysis based on data mining models. Open Life Sci. 2023. https:\/\/doi.org\/10.1515\/biol-2022-0643.","journal-title":"Open Life Sci"},{"key":"305_CR36","doi-asserted-by":"publisher","DOI":"10.1515\/biol-2022-0609","author":"F Iqbal","year":"2023","unstructured":"Iqbal F, Satti MI, Irshad A, Shah MA. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach. Open Life Sci. 2023. https:\/\/doi.org\/10.1515\/biol-2022-0609.","journal-title":"Open Life Sci"},{"issue":"8","key":"305_CR37","first-page":"51","volume":"10","author":"WM Alshwaish","year":"2019","unstructured":"Alshwaish WM, Alabdulhafith MI. Mortality prediction based on imbalanced new born and perinatal period data. Int J Adv Comput Sci Appl. 2019;10(8):51\u201360.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"305_CR38","doi-asserted-by":"publisher","DOI":"10.1186\/s12887-023-03838-0","author":"TW Gudayu","year":"2023","unstructured":"Gudayu TW. Epidemiology of neonatal mortality: a spatial and multilevel analysis of the 2019 mini\u2014Ethiopian demographic and health survey data. BMC Pediatr. 2023. https:\/\/doi.org\/10.1186\/s12887-023-03838-0.","journal-title":"BMC Pediatr"},{"issue":"6","key":"305_CR39","doi-asserted-by":"publisher","first-page":"43","DOI":"10.20943\/01201706.4351","volume":"14","author":"S Maheshwari","year":"2017","unstructured":"Maheshwari S. A review on class imbalance problem: analysis and potential solutions. Int J Comput Sci Issues. 2017;14(6):43\u201351. https:\/\/doi.org\/10.20943\/01201706.4351.","journal-title":"Int J Comput Sci Issues"},{"issue":"9","key":"305_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/bmjopen-2022-066931","volume":"13","author":"T Yan","year":"2023","unstructured":"Yan T, et al. Risk factors for neonatal mortality: an observational cohort study in Sarlahi district of rural southern Nepal. BMJ Open. 2023;13(9):1\u201315. https:\/\/doi.org\/10.1136\/bmjopen-2022-066931.","journal-title":"BMJ Open"},{"issue":"3","key":"305_CR41","doi-asserted-by":"publisher","first-page":"696","DOI":"10.3390\/pediatric16030059","volume":"16","author":"M Nabila","year":"2024","unstructured":"Nabila M, Baidani A, Mourajid Y, Chebabe M, Abderraouf H. Analysis of risk determinants of neonatal mortality in the last decade: a systematic literature review (2013\u20132023). Pediatr Rep. 2024;16(3):696\u2013716.","journal-title":"Pediatr Rep"},{"issue":"October","key":"305_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fped.2018.00277","volume":"6","author":"L Liang","year":"2018","unstructured":"Liang L, et al. Predictors of mortality in neonates and infants hospitalized with sepsis or serious infections in developing countries: a systematic review. Front Pediatr. 2018;6(October):1\u201312. https:\/\/doi.org\/10.3389\/fped.2018.00277.","journal-title":"Front Pediatr"},{"key":"305_CR43","unstructured":"B. L. Thomas, \u201cStudy reveals critical factors behind neonatal mortality,\u201d pp. 1\u20135, 2023."},{"issue":"1","key":"305_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12889-019-7651-y","volume":"19","author":"R Pabayo","year":"2019","unstructured":"Pabayo R, Cook DM, Harling G, Gunawan A, Rosenquist NA, Muennig P. State-level income inequality and mortality among infants born in the United States 2007\u20132010: a cohort study. BMC Public Health. 2019;19(1):1\u20139.","journal-title":"BMC Public Health"},{"issue":"1","key":"305_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/bmjpo-2024-003067","volume":"8","author":"FCS Veloso","year":"2024","unstructured":"Veloso FCS, Barros CRA, Kassar SB, Gurgel RQ. Neonatal death prediction scores: a systematic review and meta-analysis. BMJ Paediatr Open. 2024;8(1):1\u201310. https:\/\/doi.org\/10.1136\/bmjpo-2024-003067.","journal-title":"BMJ Paediatr Open"},{"key":"305_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41390-024-03773-5","author":"BA Sullivan","year":"2024","unstructured":"Sullivan BA, et al. Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition. Pediatr Res. 2024. https:\/\/doi.org\/10.1038\/s41390-024-03773-5.","journal-title":"Pediatr Res"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00305-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00305-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00305-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T10:45:10Z","timestamp":1757414710000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00305-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,22]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["305"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00305-w","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,22]]},"assertion":[{"value":"30 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"220"}}