{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:50:33Z","timestamp":1791474633775,"version":"4.3.4"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Artificial intelligence is changing the practice of healthcare. While it is essential to employ such solutions, making them transparent to medical experts is more critical. Most of the previous work presented disease prediction models, but did not explain them. Many healthcare stakeholders do not have a solid foundation in these models. Treating these models as \u2018black box\u2019 diminishes confidence in their predictions. The development of explainable artificial intelligence (XAI) methods has enabled us to change the models into a \u2018white box\u2019. XAI allows human users to comprehend the results from machine learning algorithms by making them easy to interpret. For instance, the expenditures of healthcare services associated with unplanned readmissions are enormous. This study proposed a stacking-based model to predict 30-day hospital readmission for diabetic patients. We employed Random Under-Sampling to solve the imbalanced class issue, then utilised SelectFromModel for feature selection and constructed a stacking model with base and meta learners. Compared with the different machine learning models, performance analysis showed that our model can better predict readmission than other existing models. This proposed model is also explainable and interpretable. Based on permutation feature importance, the strong predictors were the number of inpatients, the primary diagnosis, discharge to home with home service, and the number of emergencies. The local interpretable model-agnostic explanations method was also employed to demonstrate explainability at the individual level. The findings for the readmission of diabetic patients could be helpful in medical practice and provide valuable recommendations to stakeholders for minimising readmission and reducing public healthcare costs.<\/jats:p>","DOI":"10.3390\/info13090436","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T22:11:10Z","timestamp":1663279870000},"page":"436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Explainable Stacking-Based Model for Predicting Hospital Readmission for Diabetic Patients"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5338-156X","authenticated-orcid":false,"given":"Haohui","family":"Lu","sequence":"first","affiliation":[{"name":"School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, Sydney, NSW 2037, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0091-6919","authenticated-orcid":false,"given":"Shahadat","family":"Uddin","sequence":"additional","affiliation":[{"name":"School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, Sydney, NSW 2037, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Carvalho, D.V., Pereira, E.M., and Cardoso, J.S. 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