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We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017\u20132018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180\u00a0days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model\u2019s performance. Among 358,629 hospitalized patients with cancer, 5.86% (n\u2009=\u200921,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.<\/jats:p>","DOI":"10.1038\/s41598-023-40552-4","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T16:48:57Z","timestamp":1692377337000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach"],"prefix":"10.1038","volume":"13","author":[{"given":"Sola","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ted J.","family":"Sohn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boon Peng","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanhyun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"40552_CR1","doi-asserted-by":"publisher","first-page":"941148","DOI":"10.3389\/fcvm.2022.941148","volume":"9","author":"H Chen","year":"2022","unstructured":"Chen, H. et al. 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