{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:45:38Z","timestamp":1790621138172,"version":"4.1.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Among elderly populations with concurrent type 2 diabetes mellitus (T2DM) and heart failure (HF), 30-day hospital readmission rates range 10%\u201325%. Conventional risk evaluation instruments show restricted predictive performance (AUC\u2009&amp;lt;\u20090.70) in this multimorbid group. This research aimed to construct and verify an artificial intelligence-based algorithm for assessing 30-day readmission probability in elderly T2DM-HF patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective cohort study included 870 participants \u226565 years with T2DM and HF (January 2020\u2013December 2023), randomly divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009609, 70%) and validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009261, 30%) cohorts. Variable selection utilized Least Absolute Shrinkage and Selection Operator with ten-fold cross-validation. Eight machine learning algorithms were evaluated: logistic regression, random forest, gradient boosting machines, support vector machines, neural networks, convolutional neural networks, AdaBoost, and stacking ensemble. Model interpretability was enhanced using SHapley Additive exPlanations analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Overall 30-day readmission rate was 12.4% (108\/870 patients). The Stacking Ensemble model achieved superior performance with AUC 0.867 (95% CI: 0.830\u20130.904), accuracy 79.4%, sensitivity 74.9%, and specificity 84.0%. Fourteen key predictors were identified, with C-reactive protein, estimated glomerular filtration rate, and B-type natriuretic peptide as most influential factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed a high-performing, interpretable machine learning model for predicting 30-day readmission risk, providing a valuable clinical decision-making tool.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2025.1673159","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:00:56Z","timestamp":1767769256000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning-based prediction model for 30-day readmission risk in elderly patients with type 2 diabetes mellitus and heart failure: a retrospective cohort study with SHAP 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