Selected article for: "random forest and RF random forest LR logistic regression"
Author: Xiaolong Qi; Zicheng Jiang; QIAN YU; Chuxiao Shao; Hongguang Zhang; Hongmei Yue; Baoyi Ma; Yuancheng Wang; Chuan Liu; Xiangpan Meng; Shan Huang; Jitao Wang; Dan Xu; Junqiang Lei; Guanghang Xie; Huihong Huang; Jie Yang; Jiansong Ji; Hongqiu Pan; Shengqiang Zou; Shenghong Ju
Title: Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study
  • Document date: 2020_3_3
    • ID: 2s4ifz7i_27
    Snippet: In total, 1218 features were calculated per lesion patch. First-order, shape and second-order features were extracted from original images and wavelet filter applied images using pyradiomics. 10 Two supervised learning algorithms, logistic regression (LR) and random forest (RF), were used to build the model and verify the robustness of features (supplementary file). 11 We applied 5-fold cross-validation on the training dataset to prove model perf.....
    Document: In total, 1218 features were calculated per lesion patch. First-order, shape and second-order features were extracted from original images and wavelet filter applied images using pyradiomics. 10 Two supervised learning algorithms, logistic regression (LR) and random forest (RF), were used to build the model and verify the robustness of features (supplementary file). 11 We applied 5-fold cross-validation on the training dataset to prove model performance.

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