The number of features of the model after RFE feature selection.

<p>The number of features of the model after RFE feature selection.</p>

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Bibliographic Details
Main Author:	Li Wang (15202) (author)
Other Authors:	Yu zhang (6843623) (author), Feng li (20489704) (author), Caiyun Li (10788165) (author), Hongzeng Xu (386843) (author)
Published:	2024
Subjects:	Biotechnology Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified recursive feature elimination provide valuable insights method mainly consists feature selection method extreme gradient enhancement effective feature selection class imbalance problem new method based early clinical interventions stacking ensemble model accurate mortality prediction xlink "> patient layer stacking model 949 ), et 948 ), adb 946 ), svm 942 ), rf 934 ), dt stacking model proposed proposed stacking model hospital mortality risk mortality prediction xlink "> et ), risk patients premier hospital first layer clinical decision subsequent model single model base model unique double two groups three parts thereby maximizing surviving group results show oversampling technique logistic regression leading cause january 1 identify high december 31 china based candidate models 920 ). 3061 patients 2022 diagnosed
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