{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:15:04Z","timestamp":1787238904363,"version":"build-2736575974"},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vox Sanguinis"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Objectives<\/jats:title>\n                    <jats:p>Blood donor return behaviour is frequently examined through questionnaires assessing donors' motivations, attitudes, awareness and willingness to donate. An enhanced donor invitation model that better predicts donation dynamics using specific behavioural variables is needed. This study aims to incorporate machine learning into processing blood donor datasets, thereby increasing donor return rates through precision marketing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This retrospective study was conducted between 1 January 2022 and 31 December 2023, focusing on blood donors whose records were maintained by the Kaohsiung Blood Center in Taiwan. Three machine learning models (MLMs), namely eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost), were employed. Each model underwent cross\u2010validation, and their performance was evaluated using accuracy, average precision, balanced accuracy, F1 score and log loss. The best performing model was selected to generate predictive donor return lists, which were subsequently tested.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      All three MLMs demonstrated strong predictive power in generating donor return prediction lists. The performance metrics for the three models were as follows: accuracy: 0.8093, 0.8096 and 0.8106; specificity: 0.9227, 0.9256 and 0.9229. CatBoost was found to be the best performing model. The most influential predictive factors were return donation interval and donation location. Results showed a significant improvement in donor return rates (57.99% vs. 15.54%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MLMs leveraging both behavioural and demographic variables can predict and identify high\u2010return\u2010rate donors by prioritizing return thresholds. Precision marketing significantly improves donor return rates, enabling blood banks to rapidly meet transfusion demands during emergencies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/vox.70139","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:33:36Z","timestamp":1761006816000},"page":"453-460","source":"Crossref","is-referenced-by-count":2,"title":["Prediction of blood donor return behaviour using machine learning"],"prefix":"10.1111","volume":"121","author":[{"given":"Cheng\u2010Hung","family":"Yeh","sequence":"first","affiliation":[{"name":"Taiwan Blood Services Foundation Kaohsiung Blood Center  Kaohsiung City Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5696-8123","authenticated-orcid":false,"given":"Chi\u2010Ming","family":"Hung","sequence":"additional","affiliation":[{"name":"Taiwan Blood Services Foundation Kaohsiung Blood Center  Kaohsiung City Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao\u2010Hsuan","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Industrial Management National Taiwan University of Science and Technology  Taipei City Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei\u2010Jan","family":"Su","sequence":"additional","affiliation":[{"name":"Taiwan Blood Services Foundation Kaohsiung Blood Center  Kaohsiung City Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"HouSM.Annual report of Taiwan Blood Services Foundation.2021. Available from:https:\/\/www.blood.org.tw\/blood\u2010en\/submenu?usein=2&psid=0P071531042210553838. Last accessed 17 Oct 2025."},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/voxs.12561"},{"key":"e_1_2_9_4_1","volume-title":"Big Data Key Considerations","author":"Chia H","year":"2014"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.13874"},{"key":"e_1_2_9_6_1","unstructured":"MostafaFB HasanME.Machine learning approaches for inferring liver diseases and detecting blood donors from medical diagnosis. arXiv preprint arXiv:2104.120552021."},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-5802-9_44"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.15935"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6123745"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2018.02.12"},{"key":"e_1_2_9_11_1","volume-title":"International conference on artificial neural networks","author":"Giunchiglia E","year":"2018"},{"key":"e_1_2_9_12_1","volume-title":"Advances in Neural Information Processing Systems","author":"Ke G","year":"2017"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2021.107619"},{"key":"e_1_2_9_14_1","volume-title":"Proceedings of the 32nd international conference on neural information processing systems","author":"Prokhorenkova L","year":"2018"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00369-8"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-008-0114-1"},{"key":"e_1_2_9_17_1","first-page":"487","volume-title":"Proceedings of the 20th international conference on very large data bases (VLDB)","author":"Agrawal R","year":"1994"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmrv.2023.150809"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.17861"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.17934"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.17850"},{"key":"e_1_2_9_22_1","volume-title":"Big data marketing","author":"Chen T","year":"2018"},{"key":"e_1_2_9_23_1","volume-title":"Strategic database marketing 4e: the masterplan for starting and managing a profitable, customer\u2010based marketing program","author":"Hughes AM","year":"2012"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.17582"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.17585"}],"container-title":["Vox Sanguinis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/vox.70139","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/vox.70139","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/vox.70139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:43:56Z","timestamp":1775807036000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/vox.70139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1111\/vox.70139"],"URL":"https:\/\/doi.org\/10.1111\/vox.70139","archive":["Portico"],"relation":{},"ISSN":["0042-9007","1423-0410"],"issn-type":[{"value":"0042-9007","type":"print"},{"value":"1423-0410","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,20]]}}}