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Hematoma expansion prediction based on SMOTE and XGBoost algorithm.
Li, Yan; Du, Chaonan; Ge, Sikai; Zhang, Ruonan; Shao, Yiming; Chen, Keyu; Li, Zhepeng; Ma, Fei.
Afiliación
  • Li Y; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Du C; Department of Neurosurgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Ge S; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Zhang R; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Shao Y; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Chen K; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Li Z; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Ma F; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China. Fei.Ma@xjtlu.edu.cn.
BMC Med Inform Decis Mak ; 24(1): 172, 2024 Jun 19.
Article en En | MEDLINE | ID: mdl-38898499
ABSTRACT
Hematoma expansion (HE) is a high risky symptom with high rate of occurrence for patients who have undergone spontaneous intracerebral hemorrhage (ICH) after a major accident or illness. 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 h after the occurrence of ICH, while in reality a considerable number of patients have HE after the first 6 h but within 24 h. In this study, based on the medical doctors recommendation, we focus on prediction of the occurrence of HE within 24 h, as well as the occurrence of HE every 6 h within 24 h. Based on the demographics and computer tomography (CT) image extraction information, we used the XGBoost method to predict the occurrence of HE within 24 h. 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 h at the accuracy of 0.89, 0.82, 0.87 and 0.94, indicating that the HE occurrence within 24 h can be predicted accurately by the proposed method.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Hemorragia Cerebral / Hematoma Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Hemorragia Cerebral / Hematoma Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido