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    ARTICLE

    Risk Prediction of Aortic Dissection Operation Based on Boosting Trees

    Ling Tan1, Yun Tan2, Jiaohua Qin2, Hao Tang1,*, Xuyu Xiang2, Dongshu Xie1, Neal N. Xiong3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2583-2598, 2021, DOI:10.32604/cmc.2021.017779

    Abstract During the COVID-19 pandemic, the treatment of aortic dissection has faced additional challenges. The necessary medical resources are in serious shortage, and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection. In this work, we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic. A general scheme of medical data processing is proposed, which includes five modules, namely problem definition, data preprocessing, data mining, result analysis, and knowledge application. Based on effective data preprocessing, feature analysis and boosting trees, our proposed fusion decision model can… More >

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