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Aneurysm prediction models for TAV patients exhibited mean area under the receiver-operating-characteristic curve (AUC) values above 0.8, with the absence of aortic stenosis being the primary predictor, followed by diabetes and high-sensitivity C reactive protein. Conversely, prediction models for BAV patients resulted in AUC values between 0.5 and 0.55, indicating low usefulness for predicting aortic dilatation. Classification results remained consistent across all machine learning algorithms and classic logistic regression models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion and recommendation<\/jats:title>\n                    <jats:p>Cardiovascular risk profiles appear to be more predictive of aortopathy in TAV patients than in patients with BAV. This adds evidence to the fact that BAV-associated and TAV-associated aortopathy involves different pathways to aneurysm formation and highlights the need for specific aneurysm preventions in these patients. Further, our results highlight that machine learning approaches do not outperform classical prediction methods in addressing complex interactions and non-linear relations between variables.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2022-067977","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T13:25:11Z","timestamp":1710941111000},"page":"e067977","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Predictive machine learning models for ascending aortic dilatation in patients with bicuspid and tricuspid aortic valves undergoing cardiothoracic surgery: a prospective, single-centre and observational study"],"prefix":"10.1136","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5516-4665","authenticated-orcid":false,"given":"Bamba","family":"Gaye","sequence":"first","affiliation":[{"name":"Cardiovascular Medicine Unit, Center for Molecular Medicine, 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