{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T08:12:16Z","timestamp":1778919136338,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Mother\u2019s health risks should be identified early so that the outcome of the pregnancy can be enhanced and the complications experienced during pregnancy can be minimized. This paper will design and test a leakage-regulated hybrid machine learning model to predict maternal health risk using the optimized ensemble models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>It used the publicly available UCI Maternal Health Risk dataset (n = 1015). A fixed random seed was used to stratify the dataset to 80% training and 20% independent testing subsets (42). SMOTENN resampling was only done to the training data to avoid data leakage. Internal cross-validation was resorted to as a means of hyperparameter tuning. We came up with the optimized XGBoost, blending, and hybrid stacking models. The performance of a model was measured in terms of accuracy, precision, recall, F1 score, ROC-AUC, confusion analysis, and probability mean squared error (Brier score).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The hybrid stacking model had a ROC-AUC of 0.911 and general accuracy of 80 percent over the independent test set. The model proved to be very sensitive with high-risk cases (recall = 0.85). The probability mean squared error (Brier score) was 0.07, which is good probability calibration. The hybrid framework proposed performed better in terms of discriminative capability as compared to baseline models (logistic regression, random forest, and SVM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The suggested leakage-sensitive hybrid ensemble framework offers strong and clinically significant working outcomes on maternal health risk forecasting. The results show the significance of effective validation techniques and probabilistic evaluation measures in healthcare machine learning systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261450786","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T03:48:33Z","timestamp":1778730513000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of an advanced maternal health risk detection system using enhanced XGBOOST and blending models"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1481-3577","authenticated-orcid":false,"given":"Hatem Kareem","family":"Saleem Altaie","sequence":"first","affiliation":[{"name":"Altinbas University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullahi Abdu","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Altinbas University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.06.010"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.2196\/29838"},{"key":"e_1_3_6_4_2","first-page":"16","article-title":"Prediction of maternal health risk with traditional machine learning methods","volume":"4","author":"Mutlu HB","year":"2023","unstructured":"Mutlu HB, Y\u00fccel N, Durmaz F, et al. 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Int J Appl Basic Med Res 2019; 9: 193\u2013195. https:\/\/doi.org\/10.4103\/ijabmr.IJABMR","journal-title":"Int J Appl Basic Med Res"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajogmf.2020.100250"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.whi.2020.08.007"},{"key":"e_1_3_6_13_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0276525"},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-024-07030-9"},{"key":"e_1_3_6_15_2","first-page":"161","article-title":"Comparative assessment of several effective machine learning classification methods for maternal health risk","volume":"3","author":"Raihen MN","year":"2024","unstructured":"Raihen MN, Akter S. Comparative assessment of several effective machine learning classification methods for maternal health risk. Comput J Math Stat Sci 2024; 3: 161\u2013176. https:\/\/doi.org\/10.21608\/cjmss.2024.259490.1036","journal-title":"Comput J Math Stat Sci"},{"key":"e_1_3_6_16_2","first-page":"1953","article-title":"Maternal health risk analysis using support vector machine kernels in machine learning","volume":"10","author":"Chaturvedi R","year":"2024","unstructured":"Chaturvedi R, Balmiki P, Jaiswal R, et al. Maternal health risk analysis using support vector machine kernels in machine learning. Grenze Int J Eng Technol 2024; 10: 1953.","journal-title":"Grenze Int J Eng Technol"},{"key":"e_1_3_6_17_2","unstructured":"Ahmed M. Maternal health risk dataset. UCI Machine Learning Repository. 2020; https:\/\/doi.org\/10.24432\/C5DP5D"},{"key":"e_1_3_6_18_2","volume-title":"What is label encoding in Python","author":"Great Learning Team","unstructured":"Great Learning Team. What is label encoding in Python. Great Learning. 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