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Correct prediction of the occurrence of HE in advance is critical to help the doctors to determine the next step medical treatment. Most existing studies focus only on the occurrence of HE within 6\u00a0h after the occurrence of ICH, while in reality a considerable number of patients have HE after the first 6\u00a0h but within 24\u00a0h. In this study, based on the medical doctors recommendation, we focus on prediction of the occurrence of HE within 24\u00a0h, as well as the occurrence of HE every 6\u00a0h within 24\u00a0h. Based on the demographics and computer tomography (CT) image extraction information, we used the XGBoost method to predict the occurrence of HE within 24\u00a0h. In this study, to solve the issue of highly imbalanced data set, which is a frequent case in medical data analysis, we used the SMOTE algorithm for data augmentation. To evaluate our method, we used a data set consisting of 582 patients records, and compared the results of proposed method as well as few machine learning methods. Our experiments show that XGBoost achieved the best prediction performance on the balanced dataset processed by the SMOTE algorithm with an accuracy of 0.82 and F1-score of 0.82. Moreover, our proposed method predicts the occurrence of HE within 6, 12, 18 and 24\u00a0h at the accuracy of 0.89, 0.82, 0.87 and 0.94, indicating that the HE occurrence within 24\u00a0h can be predicted accurately by the proposed method.<\/jats:p>","DOI":"10.1186\/s12911-024-02561-9","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T10:04:14Z","timestamp":1718791454000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Hematoma expansion prediction based on SMOTE and XGBoost algorithm"],"prefix":"10.1186","volume":"24","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaonan","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sikai","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruonan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhepeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"2561_CR1","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.ebiom.2019.04.040","volume":"43","author":"J Liu","year":"2019","unstructured":"Liu J, Xu H, et al. 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