{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T20:42:57Z","timestamp":1793911377634,"version":"4.8.0"},"reference-count":11,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"content-version":"vor","delay-in-days":44,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Diabetes has become a critical global health concern, particularly in regions where access to diagnostic facilities is limited. In this work, we propose a hybrid framework that combines extreme gradient boosting (XGBoost) and deep neural networks (DNNs) for early\u2010stage diabetes detection, using soft voting to generate the final ensemble predictions. The proposed framework was evaluated on two datasets: the widely used Diabetes UCI dataset and a newly collected dataset from Nepal. The ensemble method achieved 99% accuracy (ACC) with an area under the curve (AUC) of 1.00 on the Diabetes UCI dataset, and 91% ACC with a 0.96 AUC on the Nepal diabetes dataset, demonstrating its strong generalisability across distinct populations. Compared to individual models, the hybrid approach offered increased stability and a lower rate of false negatives, which is particularly important in clinical contexts. These findings highlight the potential of hybrid machine learning\u2013deep learning models as cost\u2010effective, scalable and generalisable decision\u2010support tools for diabetes risk assessment.<\/jats:p>","DOI":"10.1049\/htl2.70060","type":"journal-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:21:36Z","timestamp":1771068096000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Hybrid Ensemble Approach for Early\u2010Stage Diabetes Detection"],"prefix":"10.1049","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8512-3425","authenticated-orcid":false,"given":"Rachana","family":"Katuwal KC","sequence":"first","affiliation":[{"name":"Department of Computer Science Swansea University  Wales UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science Swansea University  Wales UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,2,14]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"\u201cHealth Topcis \u201dDiabetes accessed April 4 2024 https:\/\/www.who.int\/news\u2010room\/fact\u2010sheets\/detail\/diabetes."},{"key":"e_1_2_9_3_1","volume-title":"Global Report on Diabetes","author":"World Health Organization","year":"2016"},{"key":"e_1_2_9_4_1","unstructured":"\u201cDiabetes Global Report 2000\u20132050 \u201d accessed April 4 2024 https:\/\/diabetesatlas.org\/data\u2010by\u2010location\/global\/."},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","unstructured":"F.Karachaliou G.Simatos andA.Simatou \u201cThe Challenges in the Development of Diabetes Prevention and Care Models in Low\u2010Income Settings \u201d accessed May 11 2024 https:\/\/www.frontiersin.org\/journals\/endocrinology\/articles\/10.3389\/fendo.2020.00518\/full.","DOI":"10.3389\/fendo.2020.00518"},{"key":"e_1_2_9_6_1","unstructured":"M. M. F.Islam R.Ferdousi S.Rahman Humayra andY.Bashra \u201cDiabetes UCI Dataset \u201d accessed May 8 2024 https:\/\/www.kaggle.com\/datasets\/alakaaay\/diabetes\u2010uci\u2010dataset."},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.4103\/jfmpc.jfmpc_502_22"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742\u20106596\/1684\/1\/012062"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3047942"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22145247"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227\u2010024\u201006211\u20109"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"\u00d6. N.Erg\u00fcnandH. O.\u0130Lhan \u201cEarly Stage Diabetes Prediction Using Machine Learning Methods \u201dEuropean Journal of Science and Technology(December 2021):52\u201357 https:\/\/doi.org\/10.31590\/ejosat.1015816.","DOI":"10.31590\/ejosat.1015816"}],"container-title":["Healthcare Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl2.70060","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/htl2.70060","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl2.70060","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:28:43Z","timestamp":1789997323000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/htl2.70060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":11,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1049\/htl2.70060"],"URL":"https:\/\/doi.org\/10.1049\/htl2.70060","archive":["Portico"],"relation":{},"ISSN":["2053-3713","2053-3713"],"issn-type":[{"value":"2053-3713","type":"print"},{"value":"2053-3713","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-09-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70060"}}