{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:23:29Z","timestamp":1764937409316,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61502537","2019GK4013"],"award-info":[{"award-number":["61502537","2019GK4013"]}]},{"name":"High-tech Development and Industrialization Office","award":["61502537","2019GK4013"],"award-info":[{"award-number":["61502537","2019GK4013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Aortic dissection (AD) is a rare and high-risk cardiovascular disease with high mortality. Due to its complex and changeable clinical manifestations, it is easily missed or misdiagnosed. In this paper, we proposed an ensemble learning model based on clustering: Cluster Random under-sampling Smote\u2013Tomek Bagging (CRST-Bagging) to help clinicians screen for AD patients in the early phase to save their lives. In this model, we propose the CRST method, which combines the advantages of Kmeans++ and the Smote\u2013Tomek sampling method, to overcome an extremely imbalanced AD dataset. Then we used the Bagging algorithm to predict the AD patients. We collected AD patients\u2019 and other cardiovascular patients\u2019 routine examination data from Xiangya Hospital to build the AD dataset. The effectiveness of the CRST method in resampling was verified by experiments on the original AD dataset. Our model was compared with RUSBoost and SMOTEBagging on the original dataset and a test dataset. The results show that our model performed better. On the test dataset, our model\u2019s precision and recall rates were 83.6% and 80.7%, respectively. Our model\u2019s F1-score was 82.1%, which is 4.8% and 1.6% higher than that of RUSBoost and SMOTEBagging, which demonstrates our model\u2019s effectiveness in AD screening.<\/jats:p>","DOI":"10.3390\/ijerph19095657","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T14:49:38Z","timestamp":1651848578000},"page":"5657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cluster-Based Ensemble Learning Model for Aortic Dissection Screening"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8426-6724","authenticated-orcid":false,"given":"Yan","family":"Gao","sequence":"first","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guogang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xiangya School of Medicine, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingming","family":"Luo","sequence":"additional","affiliation":[{"name":"Xiangya School of Medicine, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijue","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2779","DOI":"10.1093\/eurheartj\/ehv178","article-title":"Corrigendum to: 2014 ESC Guidelines on the Diagnosis and Treatment of Aortic Diseases","volume":"36","author":"Erbel","year":"2015","journal-title":"Eur. 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